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To effectively guide the exploration of the code transform space for automated code evolution techniques, we present in this paper the first approach for structurally predicting code transforms at the level of AST nodes using conditional random fields (CRFs). Our approach first learns offline a probabilistic model that captures how certain code transforms are applied to certain AST nodes, and then uses the learned model to predict transforms for arbitrary new, unseen code snippets. Our approach involves a novel representation of both programs and code transforms. Specifically, we introduce the formal framework for defining the so-called AST-level code transforms and we demonstrate how the CRF model can be accordingly designed, learned, and used for prediction. We instantiate our approach in the context of repair transform prediction for Java programs. Our instantiation contains a set of carefully designed code features, deals with the training data imbalance issue, and comprises transform constraints that are specific to code. We conduct a large-scale experimental evaluation based on a dataset of bug fixing commits from real-world Java projects. The results show that when the popular evaluation metric top-3 is used, our approach predicts the code transforms with an accuracy varying from 41% to 53% depending on the transforms. Our model outperforms two baselines based on history probability and neural machine translation (NMT), suggesting the importance of considering code structure in achieving good prediction accuracy. In addition, a proof-of-concept synthesizer is implemented to concretize some repair transforms to get the final patches. The evaluation of the synthesizer on the Defects4j benchmark confirms the usefulness of the predicted AST-level repair transforms in producing high-quality patches. This work was partially supported by National Natural Science Foundation of China (Grant No. 62102233), Shandong Province Overseas Outstanding Youth Fund (Grant No. 2022HWYQ-043), Qilu Young Scholar Program of Shandong University, and Wallenberg Artificial Intelligence, the Wallenberg Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation. Some experiments were performed on resources provided by the Swedish National Infrastructure for Computing.

Context: The use of standards is considered a vital part of any engineering discipline. So one could expect that standards play an important role in Requirements Engineering (RE) as well. However, little is known about the actual knowledge and use of RE-related standards in industry. Objective: In this article, we investigate to which extent standards and related artifacts such as templates or guidelines are known and used by RE practitioners. Method: To this end, we have conducted a questionnaire-based online survey. We could analyze the replies from 90 RE practitioners using a combination of closed and open-text questions. Results: Our results indicate that the knowledge and use of standards and related artifacts in RE is less widespread than one might expect from an engineering perspective. For example, about 47% of the respondents working as requirements engineers or business analysts do not know the core standard in RE, ISO/IEC/IEEE 29148. Participants in our study mostly use standards by personal decision rather than being imposed by their respective company, customer, or regulator. Beyond insufficient knowledge, we also found cultural and organizational factors impeding the widespread adoption of standards in RE. Conclusions: Overall, our results provide empirically informed insights into the actual use of standards and related artifacts in RE practice and - indirectly - about the value that the current standards create for RE practitioners., Preprint accepted for publication at IEEE Transactions on Software Engineering and presented as Journal First at the International Requirements Engineering conference 2021. Latest update: Several smaller corrections along the creation of the final version of the manuscript

The most widely used technique to allow for parallel simulations in molecular dynamics is spatial domain decomposition, where the physical geometry is divided into boxes, one per processor. This technique can inherently produce computational load imbalance when either the spatial distribution of particles or the computational cost per particle is not uniform. This paper shows the benefits of using a hybrid MPI+OpenMP model to deal with this load imbalance. We consider LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), a prototypical molecular dynamics simulator that provides its own balancing mechanism and an OpenMP implementation for many of its modules, allowing for a hybrid setup. In this work, we extend the current OpenMP implementation of LAMMPS and optimize it and evaluate three different setups: MPI-only, MPI with the LAMMPS balance mechanism, and hybrid setup using our improved OpenMP version. This comparison is made using the five standard benchmarks included in the LAMMPS distribution plus two additional test cases. Results show that the hybrid approach can deal with load balancing problems better and more effectively (50% improvement versus MPI-only for a highly imbalanced test case) than the LAMMPS balance mechanism (only 43% improvement) and improve simulations with issues other than load imbalance. This work is partially supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P), by the Generalitat de Catalunya (2017-SGR-1414), and by the European POP CoE (GA n. 824080). This work is also funded as part of the European Union Horizon 2020 research and innovation program under grant agreement nos. 800925 (VECMA project; www.vecma.eu) and 823712 (CompBioMed2 Centre of Excellence; www.compbiomed.eu), as well as the UK EPSRC for the UK High-End Computing Consortium (grant no. EP/R029598/1).

To remain competitive, organizations are challenged to make informed design decisions in order to construct software systems with similar architectural needs. They use reference architectures to achieve interoperability of (parts of) their software, standardization of software systems among multiple actors, and faster development with templates and guidelines for designing. In this chapter, we give an overview of domain-independent reference architectures and standards available in the gray literature for practitioners, classified by technology (cloud computing, big data, Internet of Things, and artificial intelligence) and analyzed based on their characteristics (e.g., purpose and contents). The discussed reference architectures are either published by standardization organizations such as ISO/IEC and NIST or fostered by large companies such as Microsoft and IBM. This work has been partially funded by the “Beatriz Galindo” Spanish Program BEAGAL18/00064 and by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00).

Recent surveys expose that the use of Open Source Software (OSS) is increasingly becoming a need for organizations in their development projects. However, deciding a proper OSS to be adopted or to contribute to its development is a complex and error-prone task. Analyzing the OSS ecosystem (OSSECO) health may help providing information about: (1) the OSS itself (number of commits, days after the last release, etc.); and (2) their main actors (number of contributors, partners, etc.). There exist proposals that go further and provide aggregated high-level indicators (e.g. visibility as an aggregation of number of community events, number of partners, and other metrics). Nevertheless, there is a lack of useful OSSECO analysis tools to ease the decision making on which OSSECO has the health required by a potential OSS adopter or contributor. In this work, we provide OSS-CARE (OSSeCo heAlthy monitoR and analysEr), an OSS-independent, fully automatic, and real-time framework to assess OSSECO’s health. OSS-CARE supports defining the ecosystem health objectives of potential OSS adopters, OSS contributors, and even OSS managers to inspect their provided health. These objectives are defined based on a well-established model characterizing health metrics that can be potentially aggregated by using a Bayesian network technique. Moreover, the integrated monitoring and analysis components perform an automated assessment of OSSECO’s health by checking the fulfillment of the required health objectives. Furthermore, the result is shown in an appealing dashboard that may ease the complex decision making of which OSS to choose. This work has been supported by the Spanish project PID2020-117191RB-I00 funded by MCIN/AEI/10.13039/501100011033; grants RTI2018-101204-B-C21, RTI2018-101204-B-C22, PID2021-126227NB-C21, PID2021-126227NB-C22 funded by MCIN/AEI/10.13039/501100011033 and ‘‘ERDF a way of making Europe’’; grants PYC20 RE 084 US, P18-FR-2895, US-1264651, US-1381595 funded by Junta de Andalucıa/ERDF, UE, Spain; and FPU19/00666 funded by MCIN/AEI/10.13039/501100011033 and by ‘‘ESF Investing in your future’’.

In this position statement we advocate how the use of ontologies may provide a proper solution for IoT semantic data integration. With this, we will improve IoT interoperability and will facilitate the development of software applications that allow providing management and monitoring of different IoT installations in a generic and homogeneous way. This work is partially funded by: Industrial Doctorates DI-2019 from Generalitat de Catalunya, project TIN2017-87610- R from MCIN/AEI/10.13039/501100011033, FEDER “Una manera de hacer Europa”, and project 2017-SGR-1749 from Generalitat de Catalunya. Also with the support of inLab FIB at UPC and Worldsensing.

Context: Agile software development has become commonplace in software development companies due to the numerous benefits it provides. However, conducting Agile projects is demanding in Small and Medium Enterprises (SMEs), because projects start and end quickly, but still have to fulfil customers' quality requirements. Objective: This paper aims at reporting a practical experience on the use of metrics related to the software development process as a means supporting SMEs in the development of software following an Agile methodology. Method: We followed Action-Research principles in a Polish small-size software development company. We developed and executed a study protocol suited to the needs of the company, using a pilot case. Results: A catalogue of Agile development process metrics practically validated in the context of a small-size software development company, adopted by the company in their Agile projects. Conclusions: Practitioners may adopt these metrics in their Agile projects, especially if working in an SME, and customise them to their own needs and tools. Academics may use the findings as a baseline for new research work, including new empirical studies. The authors would like to thank all the members of the QRapids H2020 project consortium.

En asignaturas basadas en el desarrollo en equipo de proyectos software constatamos que: 1) más allá de las entregas puntuales, los estudiantes sienten la inquietud de no saber si están avanzando en sus objetivos de aprendizaje de manera adecuada; 2) en las evaluaciones entre pares, los estudiantes se basan en apreciaciones subjetivas y prefieren evitar el conflicto a informar sobre posibles problemas del equipo; 3) los profesores carecen de una visión precisa “en tiempo real” del desempeño de los diferentes equipos, tanto a nivel de funcionamiento interno de los equipos como a un nivel más global que permita anticipar y prevenir posibles disfunciones. Para abordar estos problemas, pretendemos sacar partido de los datos generados por las herramientas de desarrollo software que utilizan los estudiantes, principalmente de gestión de versiones de código y de gestión de proyectos, para definir métricas, factores e indicadores de aprendizaje que a su vez serán integrados, visualizados y monitorizados en un cuadro de mando. Este cuadro de mando permitirá que tanto estudiantes como profesores reciban una retroalimentación continua, precisa y objetiva del proceso de aprendizaje individual y en equipo, y que tomen decisiones informadas sobre cómo priorizar, planificar y evaluar sus actuaciones en el transcurso del proyecto. In courses based on the team development of software projects, we notice that 1) beyond scheduled deliveries, students feel concerned about not knowing if they are advancing adequately in their learning objectives; 2) in peer evaluations, students make subjective assessments and prefer to avoid conflict rather than report possible team problems; 3) teachers lack a precise “real-time” view of the performance of the different teams, both at the level of the internal functioning of the teams and at a more global level, depriving them of anticipating and preventing possible dysfunctions. To address these problems, we intend to take advantage of the data generated by the software development tools used by students, mainly code version management and project management tools, to define learning metrics, factors, and indicators that will be integrated, visualized, and monitored in a dashboard. This will allow both students and teachers to receive accurate and objective feedback on the individual and team learning process and make informed decisions about prioritizing, planning, Este trabajo ha sido financiado por la Universitat Politècnica de Catalunya (Convocatòria de projectes d'innovació docent 2021) y el proyecto con referencia PDC2021-121195-I00 financiado por MCIN/AEI/10.13039/501100011033 y por la Unión Europea NextGenerationEU/ PRTR.

OpenMP is traditionally focused on boosting performance in HPC systems. However, other domains are showing an increasing interest in the use of OpenMP by virtue of key aspects introduced in recent versions of the specification: the tasking model, the accelerator model, and other features like the requires and the assumes directives, which allow defining certain contracts. One example is the safety-critical embedded domain, where several efforts have been initiated towards the adoption of OpenMP. However, the OpenMP specification states that "application developers are responsible for correctly using the OpenMP API to produce a conforming program", being not acceptable in high integrity systems, where aspects such as reliability and resiliency have to be ensured at different levels of criticality. In this scope, programming languages like Ada propose a different paradigm by exposing fewer features to the user, and leaving the responsibility of safely exploiting the full underlying architecture to the compiler and the runtime systems, instead. The philosophy behind this kind of model is to move the responsibility of producing correct parallel programs from users to vendors. In this panel, actors from different domains involved in the use of parallel programming models for the development of high-integrity systems share their thoughts about this topic. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871669. We would also like to express our gratitude to the organizers of the HILT workshop.

The number of functionalities controlled by software on every critical real-time product is on the rise in domains like automotive, avionics and space. To implement these advanced functionalities, software applications increasingly adopt artificial intelligence algorithms that manage massive amounts of data transmitted from various sensors. This translates into unprecedented memory performance requirements in critical systems that the commonly used DRAM memories struggle to provide. High-Bandwidth Memory (HBM) can satisfy these requirements offering high bandwidth, low power and high-integration capacity features. However, it remains unclear whether the predictability and isolation properties of HBM are compatible with the requirements of critical embedded systems. In this work, we perform to our knowledge the first timing analysis of HBM. We show the unique structural and timing characteristics of HBM with respect to DRAM memories and how they can be exploited for better time predictability, with emphasis on increased isolation among tasks and reduced worst-case memory latency. This work has been partially supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GB-C21/AEI/10.13039/501100011033; the European Union’s Horizon 2020 Framework Programme under grant agreement No. 878752 (MASTECS) and agreement No. 779877 (Mont-Blanc 2020); the European Research Council (ERC) grant agreement No. 772773 (SuPerCom); and the Natural Sciences and Engineering Research Council of Canada (NSERC)

—General-Purpose GPU software is considered for use in avionics to satisfy the increased computational requirements of future systems. Therefore, it needs to be certified following the DO-178C guidance as all airborne software. In this work, we review the existing methods in the literature, we analyse their advantages and disadvantages, and we discuss how they can be combined to obtain certification with lower effort and cost. Our focus is restricted on application-level software, under the premise that successful completion of verification of avionics graphics GPU software products has been demonstrated, so their GPU compiler has been considered acceptable for these already DO-178C certified products, or existing qualified GPU compilers exist. Finally, we discuss upcoming solutions for certified general purpose GPU computing . This work was performed within the Airbus TANIAGPU Project ADS (E/200) in collaboration with the project partners Airbus Defence and Space, Madrid, Spain and CoreAVI, Canada. It was also partially supported by the European Space Agency (ESA) through the GPU4S (GPU for Space) activity, the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB and FJCI-2017-34095 (Spanish State Research Agency / http://dx.doi.org/10.13039/501100011033) and the HiPEAC Network of Excellence.

Modern services and applications need to react to changes in their context (e.g. location, memory consumption, number of users) to improve the user’s experience. To obtain this context, a monitoring infrastructure with adequate functionality and quality levels is required. But this monitoring infrastructure needs to react to the context as well, raising the need for context-aware monitoring tools. Provide a generic solution for context-aware monitoring able to effectively react to contextual changes. We have designed CAMA, a service-oriented Context-Aware Monitoring Architecture that can be easily configured, adapted and evolved according to contextual changes. CAMA implements a decoupled architecture and manages a context domain ontology for modelling the inputs, outputs and capabilities of monitoring tools. CAMA has been demonstrated in three real use cases. We have also conducted different evaluations, including an empirical study. The results of the evaluations show that (1) the overhead introduced by the architecture does not degrade the behavior of the system, except in extreme conditions; (2) the use of ontologies is not an impediment for practitioners, even when they have little knowledge about this concept; and (3) the reasoning capabilities of CAMA enable context-aware adaptations. CAMA is a solution useful for both researchers and practitioners. Researchers can use this architecture as a baseline for providing different extensions or implementing new approaches on top of CAMA that require context-aware monitoring. Practitioners may also use CAMA in their projects in order to manage contextual changes in an effective way. This work was partially supported by the Spanish project GENESIS TIN2016-79269-R, and SUPERSEDE project, funded by the European Union’s Information and Communication Technologies Programme (H2020) under Grant Agreement No 644018.

Context: Tertiary studies in software engineering (TS@SE) are widely used to synthesise evidence on a research topic systematically. As part of their protocol, TS@SE define inclusion and exclusion criteria (IC/EC) aimed at selecting those secondary studies (SS) to be included in the analysis. Aims: To provide a state of the art on the definition and application of IC/EC in TS@SE, and from the results of this analysis, we outline an emerging framework, TSICEC, to be used by SE researchers. Method: To provide the state of the art, we conducted a systematic mapping (SM) combining automatic search and snowballing over the body of SE scientific literature, which led to 50 papers after application of our own IC/EC. The extracted data was synthesised using content analysis. The results were used to define a first version of TSICEC. Results: The SM resulted in a coding schema, and a thorough analysis of the selected papers on the basis of this coding. Our TSICEC framework includes guidelines for the definition of IC/EC in TS@SE. Conclusion: This paper is a step forward establishing a foundation for researchers in two ways. As authors, understanding the different possibilities to define IC/EC and apply them to select SS. As readers, having an instrument to understand the methodological rigor upon which TS@SE may claim their findings. This work has been partially supported by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00).

A Web API (WAPI) is a type of API whose interaction with its consumers is done through the Internet. While being accessed through the Internet can be challenging, mostly when WAPIs evolve, it gives providers the possibility to monitor their usage, and understand and analyze consumers' behavior. Currently, WAPI usage is mostly logged for traffic monitoring and troubleshooting. Even though they contain invaluable information regarding consumers' behavior} they are not sufficiently used by providers. In this paper, we first consider two phases of the application development lifecycle, and based on them we distinguish two different types of usage logs, namely development logs and production logs. For each of them we show the potential analyses (e.g., WAPI usability evaluation, consumers' needs identification) that can be performed, as well as the main impediments, that may be caused by the unsuitable log format. We then conduct a case study using logs of the same WAPI from different deployments and different formats, to demonstrate the occurrence of these impediments and at the same time the importance of a proper log format. Next, based on the case study results, we present the main quality issues of WAPI log data and explain their impact on data analyses. For each of them, we give some practical suggestions on how to deal with them, as well as mitigating their root cause.

Software reference architectures have played an essential role in software systems development due to the possibility of knowledge reuse. Although increasingly adopted by industry, these architectures are not yet completely understood. This work presents a panorama on existing software reference architectures, characterizing them according to their context, goals, perspectives, application domains, design approaches, and maturity, as well as the industry involvement for their construction. For this, we planned and conducted a systematic mapping study. During last decade, the number of reference architectures in very diverse application domains has increased, resulting from efforts of industry, academia, and through their collaborations. Academic reference architectures are oriented to facilitate the reuse of architectural and domain knowledge. The industry has focused on architectures for standardization with certain maturity level. However, the great amount of architectures studied in this work have been designed without following a systematic process, and they lack the maturity to be used in real software projects. Further investigations can be oriented to gathering empirical evidences, from different sources than academic data libraries, that allow to understand how references architectures have been constructed, utilized, and maintained during the whole software life-cycle. The work herein presented has been supported by the Brazilian funding agencies FAPESP (São Paulo Research Foundation) Grants No. 2018/07437-9 and 2017/06195-9 and the CNPq (National Council for Scientific and Technological Development) Grants No. 312634/ 2018-8 and 141602/2017-1, the GENESIS project under the National Spanish Program for Research Aimed at the Challenges of Society (RETOS 2016, contract TIN2016-79269-R), and the "Beatriz Galindo" Spanish Program BEAGAL18/00064.

Context: In despite of agile and rapid software development (ARSD) being researched and applied extensively, managing quality requirements (QRs) are still challenging. As ARSD processes produce a large amount of data, measurement has become a strategy to facilitate QR management. Objective: This study aims to survey the literature related to QR management through metrics in ARSD, focusing on: bibliometrics, QR metrics, and quality-related indicators used in quality management. Method: The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. Results: We selected 61 primary studies (2001-2019). Despite a large body of knowledge and standards, there is no consensus regarding QR measurement. Terminology is varying as are the measuring models. However, seemingly different measurement models do contain similarities. Conclusion: The industrial relevance of the primary studies shows that practitioners have a need to improve quality measurement. Our collection of measures and data sources can serve as a starting point for practitioners to include quality measurement into their decision-making processes. Researchers could benefit from the identified similarities to start building a common framework for quality measurement. In addition, this could help researchers identify what quality aspects need more focus, e.g., security and usability with few metrics reported. This work has been funded by the European Union’s Horizon 2020 research and innovation program through the Q-Rapids project (grant no. 732253). This research was also partially supported by the Spanish Ministerio de Economía, Industria y Competitividad through the DOGO4ML project (grant PID2020-117191RB-I00). Silverio Martínez-Fernández worked in Fraunhofer IESE before January 2020.

Freelance, part-time, work-at-home, and other flexible jobs are changing the concept of workplace, and bringing information and content exchange problems to companies. Geographically spread corporations may use remote distribution of software and data to attend employees' demands, by exploiting emerging delivery technologies. In this context, cost-efficient software distribution is crucial to allow business evolution and make IT infrastructures more agile. On the other hand, container based virtualization technology is shaping the new trends of software deployment and infrastructure design. We envision current and future enterprise IT management trends evolving towards container based software delivery over Hybrid CDNs. This paper presents a novel cost-efficient QoS aware analytical model and a Hybrid CDN-P2P architecture for enterprise software distribution. The model would allow delivery cost minimization for a wide range of companies, from big multinationals to SMEs, using CDN-P2P distribution under various industrial hypothetical scenarios. Model constraints guarantee acceptable deployment times and keep interchanged content amounts below the bandwidth and storage network limits in our scenarios. Indeed, key model parameters account for network bandwidth, storage limits and rental prices, which are empirically determined from their offered values by the commercial delivery networks KeyCDN, MaxCDN, CDN77 and BunnyCDN. This preliminary study indicates that MaxCDN offers the best cost-QoS trade-off. The model is implemented in the network simulation tool PeerSim, and then applied to diverse testing scenarios by varying company types, number and profile (either, technical or administrative) of employees and the number and size of content requests. Hybrid simulation results show overall economic savings between 5\% and 20\%, compared to just hiring resources from a commercial CDN, while guaranteeing satisfactory QoS levels in terms of deployment times and number of served requests. This work was partially supported by Generalitat de Catalunya under the SGR Program (2017-SGR-962) and the RIS3CAT DRAC Project (001-P-001723). We have also received funding from Ministry of Science and Innovation (Spain) under the project EQC2019-005653-P.

Los requisitos de calidad (también llamados requisitos no-funcionales) son aquellos que permiten asegurar la calidad del software. Incluyen aspectos muy diversos, como disponibilidad, seguridad, rendimiento, escalabilidad, portabilidad y usabilidad, entre otros. Los continuos avances tecnológicos, como el software en la nube o el Internet de las Cosas, presentan nuevos retos en el desarrollo del software para poder garantizar un nivel de calidad satisfactorio de dichos aspectos. Asimismo, las metodologías de desarrollo ágil, cuyo uso viene en aumento, tales como SCRUM, XP, Kanban, no dan el soporte necesario para la gestión de dichos requisitos de calidad. Con el fin de facilitar a los ingenieros del software la toma de decisiones sobre el nivel de calidad necesario en un proyecto, es imprescindible conocer de antemano: 1) qué criterios se van a tener en cuenta para verificar, priorizar, planificar y/o negociar los requisitos de calidad. Asimismo, es necesario: 2) precisar cómo se van a evaluar dichos criterios, y 3) identificar qué factores del contexto del proyecto pueden afectar dicha evaluación. Para intentar dar respuesta a estas 3 cuestiones o preguntas de investigación, los autores de este capítulo han diseñado y están llevando a cabo un estudio sistemático de la literatura. Este trabajo presenta para su discusión la descripción de la metodología seguida en ese estudio, así como algunos de los resultados preliminares obtenidos durante su ejecución.

Increasingly adopted in critical application domains, self-adaptive systems (SaSs) present a particular ability to modify their behavior or configuration at runtime autonomously. The architectural decision-making activity in an SaS can be supported by reusing proven software structure configuration. This work was supported by Brazilian funding agencies FAPESP (Grants 2018/07437-9 and 2017/06195-9) and CNPq (Grant 312634/2018-8) and by the GENESIS Spanish project under Contract TIN2016-79269-R and the Beatriz Galindo programme.

[Context and motivation] Modern software engineering processes have shifted from traditional upfront requirements engineering (RE) to a more continuous way of conducting RE, particularly including data-driven approaches. [Question/problem] However, current research on data-driven RE focuses more on leveraging certain techniques such as natural language processing or machine learning than on making the concept fit for facilitating its use in the entire software development process. [Principal ideas/results] In this paper, we propose a research agenda composed of six distinct research directions. These include a data-driven RE infrastructure, embracing data heterogeneity, context-aware adaptation, data analysis and decision support, privacy and confidentiality, and finally process integration. Each of these directions addresses challenges that impede the broader use of data-driven RE. [Contribution] For researchers, our research agenda provides topics relevant to investigate. For practitioners, overcoming the underlying challenges with the help of the proposed research will allow to adopt a data-driven RE approach and facilitate its seamless integration into modern software engineering. For users, the proposed research will enable the transparency, control, and security needed to trust software systems and software providers. This work has been supported by: the Spanish project GENESIS (TIN2016-79269-R), the Christian Doppler Forschungsgesellschaft, the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Austrian Science Fund (FWF) under the grant numbers J3998-N31, P28519-N31, and P30525-N31.

Software resource usage testing, including execution time bounds and memory, is a mandatory validation step during the integration of safety-related real-time systems. However, the inherent complexity of Autonomous Driving (AD) systems challenges current practice for resource usage testing. This paper exposes the difficulties to perform resource usage testing for AD frameworks by analyzing a complex and critical module of an AD framework, and provides some guidelines and practical evidence on how resource usage testing can be effectively performed, thus enabling end users to validate their safety-related real-time AD frameworks. This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P, the UP2DATE European Union’s Horizon 2020 (H2020) research and innovation programme under grant agreement No 871465, and the HiPEAC Network of Excellence. MINECO partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717) and Leonidas Kosmidis under Juan de la Cierva-Formación postdoctoral fellowship (FJCI-2017-34095)

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.

In this work, we propose the use of a genetic-algorithm-based attack against machine learning classifiers with the aim of ‘stealing’ users’ biometric actigraphy profiles from health related sensor data. The target classification model uses daily actigraphy patterns for user identification. The biometric profiles are modeled as what we call impersonator examples which are generated based solely on the predictions’ confidence score by repeatedly querying the target classifier. We conducted experiments in a black-box setting on a public dataset that contains actigraphy profiles from 55 individuals. The data consists of daily motion patterns recorded with an actigraphy device. These patterns can be used as biometric profiles to identify each individual. Our attack was able to generate examples capable of impersonating a target user with a success rate of 94.5%. Furthermore, we found that the impersonator examples have high transferability to other classifiers trained with the same training set. We also show that the generated biometric profiles have a close resemblance to the ground truth profiles which can lead to sensitive data exposure, like revealing the time of the day an individual wakes-up and goes to bed.

Applications typically communicate with each other, accessing and exposing data and features by using Application Programming Interfaces (APIs). Even though API consumers expect APIs to be steady and well established, APIs are prone to continuous changes, experiencing different evolutive phases through their lifecycle. These changes are of different types, caused by different needs and are affecting consumers in different ways. In this paper, we identify and classify the changes that often happen to APIs, and investigate how all these changes are reflected in the documentation, release notes, issue tracker and API usage logs. The analysis of each step of a change, from its implementation to the impact that it has on API consumers, will help us to have a bigger picture of API evolution. Thus, we review the current state of the art in API evolution and, as a result, we define a classification framework considering both the changes that may occur to APIs and the reasons behind them. In addition, we exemplify the framework using a software platform offering a Web API, called District Health Information System (DHIS2), used collaboratively by several departments of World Health Organization (WHO).

Background: Despite the growth in the use of software analytics platforms in industry, little empirical evidence is available about the challenges that practitioners face and the value that these platforms provide. Aim: The goal of this research is to explore the benefits of using a software analytics platform for practitioners managing quality. Method: In a technology transfer project, a software analytics platform was incrementally developed between academic and industrial partners to address their software quality problems. This paper focuses on exploring the value provided by this software analytics platform in two pilot projects. Results: Practitioners emphasized major benefits including the improvement of product quality and process performance and an increased awareness of product readiness. They especially perceived the semi-automated functionality of generating quality requirements by the software analytics platform as the benefit with the highest impact and most novel value for them. Conclusions: Practitioners can benefit from modern software analytics platforms, especially if they have time to adopt such a platform carefully and integrate it into their quality assurance activities., Comment: This is an Author's Accepted Manuscript of a paper consisting of a post-peer-review, pre-copyedit version of a paper accepted at the 13th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2019. The final authenticated version is available online: https://ieeexplore.ieee.org/document/8870162

This work reports the objectives, current state, and outcomes of the Q-Rapids H2020 project. Q-Rapids (Quality-Aware Rapid Software Development) proposes a data-driven approach to the production of software following very short development cycles. The focus of Q-Rapids is on quality aspects, represented through quality requirements. The Q-Rapids platform, which is the tangible software asset emerging from the project, mines software repositories and usage logs to identify candidate quality requirements that may ameliorate the values of strategic indicators like product quality, time to market or team productivity. Four companies are providing use cases to evaluate the platform and associated processes.

International audience; Context: Managing Non-Functional Requirements (NFRs) in software projects is challenging, and projects that adopt Model-Driven Development (MDD) are no exception. Although several methods and techniques have been proposed to face this challenge, there is still little evidence on how NFRs are handled in MDD by practitioners. Knowing more about the state of the practice may help researchers to steer their research and practitioners to improve their daily work. Objective: In this paper, we present our findings from an interview-based survey conducted with practitioners working in 18 different companies from 6 European countries. From a practitioner's point of view, the paper shows what barriers and benefits the management of NFRs as part of the MDD process can bring to companies, how NFRs are supported by MDD approaches, and which strategies are followed when (some) types of NFRs are not supported by MDD approaches. Results: Our study shows that practitioners perceive MDD adoption as a complex process with little to no tool support for NFRs, reporting productivity and maintainability as the types of NFRs expected to be supported when MDD is adopted. But in general, companies adapt MDD to deal with NFRs. When NFRs are not supported, the generated code is sometimes changed manually, thus compromising the maintainability of the software developed. However, the interviewed practitioners claim that the benefits of using MDD outweight the extra effort required by these manual adaptations. Conclusion: Overall, the results indicate that it is important for practitioners to handle NFRs in MDD, but further research is necessary in order to lower the barrier for supporting a broad spectrum of NFRs with MDD. Still, much conceptual and tool implementation work seems to be necessary to lower the barrier of integrating the broad spectrum of NFRs in practice.

Software quality poses continuously new challenges in software development, including aspects related to both software development and system usage, which significantly impact the success of software systems. The Q-Rapids H2020 project defines an evidence-based, data-driven quality-aware rapid software development methodology. Quality requirements (QRs) are incrementally elicited, refined and improved based on data gathered from software repositories, project management tools, system usage and quality of service. This data is analysed and aggregated into quality-related key strategic indicators (e.g., development effort required to include a given QR in the next development cycle) which are presented to decision makers using a highly informative dashboard. The Q-Rapids platform is being evaluated in-premises by the four companies participating in the consortium, reporting useful lessons learned and directions for new development.

Autosubmit GUI is a front-end software developed using Javascript and ReactJS that aims to provide users with complex information from the workflow execution of scientific experiments (managed by Autosubmit) in any system, but mainly High-Performance Computing (HPC) platforms. Autosubmit (D. Manubens-Gil, 2016) is a Python-based workflow manager able to handle complex tasks involving different sub-steps (e.g., scientific computational experiments). These steps or jobs are executed in one or multiple computing systems (platforms), from High Performance Computers to small clusters or workstations. The workflow manager is able to orchestrate the jobs that constitute the workflow while respecting their dependencies and handling errors. This front-end software consumes information served by an API (Autosubmit API (Autosubmit API, 2020)) that collects data from the execution of experiment workflows. An experiment can be seen as a task that can be decomposed into different sub-steps with established interdependencies. The execution of an experiment and its jobs may generate a high amount of information that needs to be processed so users can visualize it. Autosubmit API summarizes this information and presents it as API requests. Autosubmit GUI consumes these API requests and shows the information available in a condensed, comprehensive, and dynamic way. Autosubmit GUI uses three highly popular and useful libraries: FancyTree (FancyTree, 2020), vis.js (Vis.js, 2020), and react-google-charts (React Google Charts, 2020); among other web resources that facilitate the visualization of information and information updates. The visual approach to experiment workflow management is not new in the High-Performance Computing scenario. As two widely adopted tools, we have Cylc (Cylc, 2020) and ecFlow (ecFlow, 2020). Autosubmit GUI attempts to present a development template for those willing to work in a web environment to develop tools for progress monitoring purposes

Decision support systems (DSS) provide a unified analytical view of business data to better support decision-making processes. Such systems have shown a high level of user satisfaction and return on investment. However, several surveys stress the high failure rate of DSS projects. This problem results from setting the wrong requirements by approaching DSS in the same way as operational systems, whereas a specific approach is needed. Although this is well-known, there is still a surprising gap on how to address requirements engineering (RE) for DSS.; To overcome this problem, we conducted a systematic mapping study to identify and classify the literature on DSS from an RE perspective. Twenty-seven primary studies that addressed the main stages of RE were selected, mapped, and classified into 39 models, 27 techniques, and 54 items of guidance. We have also identified a gap in the literature on how to design the DSS main constructs (typically, the data warehouse and data flows) in a methodological manner from the business needs. We believe this study will help practitioners better address the RE stages of DSS projects.

Information systems, which are responsible for driving many processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the processes underlying an information system. A key challenge in process mining is discovery: Given a log, derive a formal process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the process discovery problem.

Software organizations want to be able to base their decisions on the latest set of available data and the real-time analytics derived from them. In order to support "real-time enterprise" for software organizations and provide information transparency for diverse stakeholders, we integrate heterogeneous data sources about software analytics, such as static code analysis, testing results, issue tracking systems, network monitoring systems, etc. To deal with the heterogeneity of the underlying data sources, we follow an ontology-based data integration approach in this paper and define an ontology that captures the semantics of relevant data for software analytics. Furthermore, we focus on the integration of such data sources by proposing two approaches: a static and a dynamic one. We first discuss the current static approach with a predefined set of analytic views representing software quality factors and further envision how this process could be automated in order to dynamically build custom user analysis using a semi-automatic platform for managing the lifecycle of analytics infrastructures., This is an author's accepted manuscript of a paper to be published by ACM in the 12th International Workshop on Real-Time Business Intelligence and Analytics (BIRTE@VLDB) 2018. The final authenticated version will be available through https://doi.org/10.1145/3242153.3242159

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage., This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available online

[EN] Context Organisational reengineering, continuous process improvement, alignment among complementary analysis perspectives, and information traceability are some current motivations to promote investment and scientific effort for integrating goal and business process perspectives. Providing support to integrate information systems analysis becomes a challenge in this complex setting. Objective The GoBIS framework integrates two goal and business process modelling approaches: i⁎ (a goal-oriented modelling method) and Communication Analysis (a communication-oriented business process modelling method). Method In this paper, we describe the methodological integration of both methods with the aim of fulfilling several criteria: i) to rely on appropriate theories; ii) to provide abstract and concrete syntaxes; iii) to provide scenarios of application; iv) to develop tool support; v) to provide demonstrable benefits to potential adopters. Results We provide guidelines for using the two modelling methods in a top-down analysis scenario. The guidelines are validated by means of a comparative experiment and a focus-group session with students. Conclusions From a practitioner viewpoint (modeller and/or analyst), the guidelines facilitate the traceability between goal and business process models, the experimental results highlight the benefits of GoBIS in performance and usability perceptions, and demonstrate an improvement on the completeness of the latter having an impact on efficiency. From a researcher perspective, the validation has produced useful feedback for future research., This work has been supported by the Spanish MICINN Project ProS-Req (TIN2010-19130-C02-01, TIN2010-19130-C02-02) and EOSSAC (TIN2013-44641-P); the Generalitat Valenciana Project IDEO (PROMETEOII/2014/039); the FPI-UPV Pre-Doctoral Grant; the European Commission Project CaaS (FP7 611351); and the ERDF Structural Funds.

Open Source Software (OSS) has become a strategic asset for a number of reasons, such as short time-to-market software delivery, reduced development and maintenance costs, and its customization capabilities. Therefore, organizations are increasingly becoming OSS adopters, either as a result of a strategic decision or because it is almost unavoidable nowadays, given the fact that most commercial software also relies at some extent in OSS infrastructure. The way in which organizations adopt OSS affects and shapes their businesses. Therefore, knowing the impact of different OSS adoption strategies in the context of an organization may help improving the processes undertaken inside this organization and ultimately pave the road to strategic moves. In this paper, we propose to model OSS adoption strategies using a goal-oriented notation, in which different actors state their objectives and dependencies on each other. These models describe the consequences of adopting one such strategy or another: which are the strategic and operational goals that are supported, which are the resources that emerge, etc. The models rely on an OSS ontology, built upon a systematic literature review, which comprises the activities and resources that characterise these strategies. Different OSS adoption strategy models arrange these ontology elements in diverse ways. In order to assess which is the OSS adoption strategy that better fits the organization needs, the notion of model coverage is introduced, which allows to measure the degree of concordance among every strategy with the model of the organization by comparing the respective models. The approach is illustrated with an example of application in a big telecommunications company.

This contribution proposes software infrastructure to support new types of learning methodologies and resources based on collaborative knowledge engineering by means of an innovative application framework called the virtualized collaborative sessions framework (VCSF). The VCSF helps meet challenging collaborative knowledge engineering requirements in online learning, such as increasing group members’ learning performance during the on-line collaborative learning process. In turn, systematic application of the VCSF platform enriched with semantic knowledge engineering technologies enables e-learning developers to leverage successful collaborative learning experiences in a software reuse fashion while saving development time and effort. The framework is prototyped and successfully tested in real environments, thus showing the software reuse capability and the collaborative knowledge engineering benefits of the VCSF approach. The research reported in this paper was undertaken within the ALICE project funded through the European 7th Framework Program (FP7).

Context: Model-Driven Development (MDD) and Service-Oriented Architecture (SOA) are two challenging research areas in software engineering. MDD is about improving software development whilst SOA is a service-based conceptual development style, therefore investigating the available proposals in the literature to use MDD when developing SOA may be insightful. However, no studies have been found with this purpose. Objective: This work aims at assessing the state of the art in MDD for SOA systems. It mainly focuses on: what are the characteristics of MDD approaches that support SOA; what types of SOA are supported; how do they handle non-functional requirements. Method: We conducted a mapping study following a rigorous protocol. We identified the representative set of venues that should be included in the study. We applied a search string over the set of selected venues. As result, 129 papers were selected and analysed (both frequency analysis and correlation analysis) with respect to the defined classification criteria derived from the research questions. Threats to validity were identified and mitigated whenever possible. Results: The analysis allows us to answer the research questions. We highlight: (1) predominance of papers from Europe and written by researchers only; (2) predominance of top-down transformation in software development activities; (3) inexistence of consolidated methods; (4) significant percentage of works without tool support; (5) SOA systems and service compositions more targeted than single services and SOA enterprise systems; (6) limited use of metamodels; (7) very limited use of NFRs; and (8) limited application in real cases. Conclusion: This mapping study does not just provide the state of the art in the topic, but also identifies several issues that deserve investigation in the future, for instance the need of methods for activities other than software development (e.g., migration) or the need of conducting more real case studies.

Software reference architectures provide templates and guidelines for designing systems in a particular domain. Companies use them to achieve interoperability of (parts of) their software, standardization, and faster development. In contrast to system-specific software architectures that "emerge" during development, reference architectures dictate significant parts of the software design early on. Agile software development frameworks (such as Scrum) acknowledge changing software requirements and the need to adapt the software design accordingly. In this paper, we present lessons learned about how reference architectures interact with Scrum (the most frequently used agile process framework). These lessons are based on observing software development projects in five companies. We found that reference architectures can support good practice in Scrum: They provide enough design upfront without too much effort, reduce documentation activities, facilitate knowledge sharing, and contribute to "architectural thinking" of developers. However, reference architectures can impose risks or even threats to the success of Scrum (e.g., to self-organizing and motivated teams).

Context: Software Reference Architectures (SRAs) play a fundamental role for organizations whose business greatly depends on the efficient development and maintenance of complex software applications. However, little is known about the real value and risks associated with SRAs in industrial practice. Objective: To investigate the current industrial practice of SRAs in a single company from the perspective of different stakeholders. Method An exploratory case study that investigates the benefits and drawbacks perceived by relevant stakeholders in nine SRAs designed by a multinational software consulting company. Results: The study shows the perceptions of different stakeholders regarding the benefits and drawbacks of SRAs (e.g., both SRA designers and users agree that they benefit from reduced development costs; on the contrary, only application builders strongly highlighted the extra learning curve as a drawback associated with mastering SRAs). Furthermore, some of the SRA benefits and drawbacks commonly highlighted in the literature were remarkably not mentioned as a benefit of SRAs (e.g., the use of best practices). Likewise, other aspects arose that are not usually discussed in the literature, such as higher time-to-market for applications when their dependencies on the SRA are managed inappropriately. Conclusions: This study aims to help practitioners and researchers to better understand real SRAs projects and the contexts where these benefits and drawbacks appeared, as well as some SRA improvement strategies. This would contribute to strengthening the evidence regarding SRAs and support practitioners in making better informed decisions about the expected SRA benefits and drawbacks. Furthermore, we make available the instruments used in this study and the anonymized data gathered to motivate others to provide similar evidence to help mature SRA research and practice.

The success and intensive use of social networks makes strategies for efficient document location a hot topic of research. In this paper, we propose a common vector space to describe documents and users to create a social network based on affinities, and explore epidemic routing to recommend documents according to the user’s interests. Furthermore, we propose the creation of a SoftDHT structure to improve the recommendation results. Using these mechanisms, an efficient document recommender system with a fast organization of clusters of users based on their affinity can be provided, preventing the creation of unlinked communities. We show through simulations that the proposed system has a short convergence time and presents a high recall ratio.

One of the most critical activities in software product development is the decisional process that assigns features to subsequent releases under technical, resource, risk, and budget constraints. This decision-centric process is referred to as software release planning (SRP). This briefing will expose a state of the art on SRP. A survey of the most relevant approaches will be presented. Emphasis will be made on their applicability (concerning e.g. type of development process - being more predictive versus more adaptive, type of system - commercial, open source product or mobile app), tool support and degree of validation in industry. One of these approaches, EVOLVE, will be analysed in detail.

We investigate several variants of the homomorphism problem: given two relational structures, is there a homomorphism from one to the other? The input structures are possibly infinite, but definable by first-order interpretations in a fixed structure. Their signatures can be either finite or infinite but definable. The homomorphisms can be either arbitrary, or definable with parameters, or definable without parameters. For each of these variants, we determine its decidability status.

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).

The success of software development using third party components highly depends on the ability to select a suitable component for the intended application. The evidence shows that there is limited knowledge about current industrial OTS selection practices. As a result, there is often a gap between theory and practice, and the proposed methods for supporting selection are rarely adopted in the industrial practice. This paper's goal is to investigate the actual industrial practice of component selection in order to provide an initial empirical basis that allows the reconciliation of research and industrial endeavors. The study consisted of semi-structured interviews with 23 employees from 20 different software-intensive companies that mostly develop web information system applications. It provides qualitative information that help to further understand these practices, and emphasize some aspects that have been overlooked by researchers. For instance, although the literature claims that component repositories are important for locating reusable components; these are hardly used in industrial practice. Instead, other resources that have not received considerable attention are used with this aim. Practices and potential market niches for software-intensive companies have been also identified. The results are valuable from both the research and the industrial perspectives as they provide a basis for formulating well-substantiated hypotheses and more effective improvement strategies.

Register window is an architectural technique that reduces memory operations required to save and restore registers across procedure calls. Its effectiveness depends on the size of the register file. Such register requirements are normally increased for out-of-order execution because it requires registers for the in-flight instructions, in addition to the architectural ones. However, a large register file has an important cost in terms of area and power and may even affect the cycle time. In this paper, we propose a software/hardware early register release technique that leverage register windows to drastically reduce the register requirements, and hence, reduce the register file cost. Contrary to the common belief that out-of-order processors with register windows would need a large physical register file, this paper shows that the physical register file size may be reduced to the bare minimum by using this novel microarchitecture. Moreover, our proposal has much lower hardware complexity than previous approaches, and requires minimal changes to a conventional register window scheme. Performance studies show that the proposed technique can reduce the number of physical registers to the number of logical registers plus one (minimum number to guarantee forward progress) and still achieve almost the same performance as an unbounded register file.

In this paper, we present HiME, a tool for editing i* models. The distinguishing characteristic of HiME is its ability to deal with inheritance. It includes specific operations for declaring an actor as heir of another and then stating the relationships between the intentional elements of both actors.

Context: Several empirical studies investigated the benefits and drawbacks of acquiring a Software Reference Architecture (SRA) to construct a family of software systems with similar architectural needs. However, these empirical results have not been synthesized by any study yet. Such synthesized evidence is essential to make informed decisions whether or not to adopt an SRA in an organization. Goal: To aggregate existing empirically- grounded evidence about the benefits and drawbacks of SRAs, aiming at supporting organizations' decision making on their adoption. Method: To identify primary studies in the technical literature through a systematic literature review, and then, use the Structured Synthesis Method (SSM) to aggregate qualitative and quantitative evidence through the use of diagrammatic models. Results: From the five identified primary studies, five SRA benefits have considerably increased their belief value after aggregation: interoperability of software systems, reduced development costs, improved communication among stakeholders, reduced risk, and reduced time- to-market. Also, one drawback of SRAs has increased its belief value: the required learning curve for developers. Conclusions: The aggregated results consolidate knowledge and confidence on some of the studied SRA effects. The commonly reported effects showed a clear increment of their belief and pointed out to broader generalization. The effects that did not show any belief increment are important to detect areas requiring further evidence to reach a higher degree of consolidation. Practitioners might benefit from these results to support the decision of adopting an SRA in practice.

International audience; Model-Driven Development (MDD) is no longer a novel development paradigm. It has become mature from a research perspective and recent studies show its adoption in industry. Still, some issues remain a challenge. Among them, we are interested in the treatment of non-functional requirements (NFRs) in MDD processes. Very few MDD approaches have been reported to deal with NFRs (and they do it in a limited way). However, it is clear that NFRs need to be considered somehow in the final product of the MDD process. To better understand how NFRs are integrated into the existing MDD approaches, we have initiated the NFR4MDD project, a multinational empirical study, based on interviews with companies working on MDD projects. Our project aims at surveying the state of the practice for this topic. In this paper, we summarize our research protocol and present the current status of our study. The discussion will focus on the peculiarities of our study's context and organization involving about 20 researchers from 8 European countries.

Valid states of data are those satisfying a set of constraints. Therefore, efficiently checking whether some constraint has been violated after a data update is an important problem in data management. We tackle this problem by incrementally checking OCL constraint violations by means of SQL queries. Given an OCL constraint, we obtain a set of SQL queries that returns the data that violates the constraint. In this way, we can check the validity of the data by checking the emptiness of these queries. The queries that we obtain are incremental since they are only executed when some relevant data update may violate the constraint, and they only examine the data related to the update.

A large number of complex networks, both natural and artificial, share the presence of highly heterogeneous, scale-free degree distributions. A few mechanisms for the emergence of such patterns have been suggested, optimization not being one of them. In this letter we present the first evidence for the emergence of scaling (and smallworldness) in software architecture graphs from a well-defined local optimization process. Although the rules that define the strategies involved in software engineering should lead to a tree-like structure, the final net is scale-free, perhaps reflecting the presence of conflicting constraints unavoidable in a multidimensional optimization process. The consequences for other complex networks are outlined., 6 pages, 2 figures. Submitted to Europhysics Letters. Additional material is available at http://complex.upc.es/~sergi/software.htm