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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.

Space missions need software in order to be aware of the occurrence of geometric events in different phases of their project lifecycle. However, current software packages that provide capabilities to search for geometric events might lack desirable software qualities such as extensibility and reusability, and some conflate the definition of events with the search for the same. In order to improve the state of the art, we propose a framework named Tychonis that, via metamodeling techniques and conscious use of object-oriented design best practices, (i) can integrate with current and future mission software, (ii) enables end-users to extend opportunities and search algorithms without modifying the tools that use the framework, and (iii) promotes the cross-mission reusability of the framework and its extensions. These attributes make it less costly for projects to develop software to search for geometric events. Tychonis is provided as a software library that allows missions to add their own data structures and constructs to the framework, including geometric event types and search algorithms. This is accomplished with Tychonis’ use of the Eclipse Modeling Framework (EMF) to capture its metamodel, which enables the generation of Java classes that represent built-in or user-developed Tychonis constructs. In the present paper, we elaborate on the impetus for the development of Tychonis, present its proposed design and use, and discuss growth opportunities for future consideration.

Background Mobile health has become a major vehicle of support for people living with diabetes. Accordingly, the availability of mobile apps for diabetes has been steadily increasing. Most of the previous reviews of diabetes apps have focused on the apps’ features and their alignment with clinical guidelines. However, there is a lack of knowledge on the actual compliance of diabetes apps with privacy and data security guidelines. Objective The aim of this study was to assess the levels of privacy of mobile apps for diabetes to contribute to the raising of awareness of privacy issues for app users, developers, and governmental data protection regulators. Methods We developed a semiautomatic app search module capable of retrieving Android apps’ privacy-related information, particularly the dangerous permissions required by apps, with the aim of analyzing privacy aspects related to diabetes apps. Following the research selection criteria, the original 882 apps were narrowed down to 497 apps that were included in the analysis. Results Approximately 60% of the analyzed diabetes apps requested potentially dangerous permissions, which pose a significant risk to users’ data privacy. In addition, 28.4% (141/497) of the apps did not provide a website for their privacy policy. Moreover, it was found that 40.0% (199/497) of the apps contained advertising, and some apps that claimed not to contain advertisements actually did. Ninety-five percent of the apps were free, and those belonging to the “medical” and “health and fitness” categories were the most popular. However, app users do not always realize that the free apps’ business model is largely based on advertising and, consequently, on sharing or selling their private data, either directly or indirectly, to unknown third parties. Conclusions The aforementioned findings confirm the necessity of educating patients and health care providers and raising their awareness regarding the privacy aspects of diabetes apps. Therefore, this research recommends properly and comprehensively training users, ensuring that governments and regulatory bodies enforce strict data protection laws, devising much tougher security policies and protocols in Android and in the Google Play Store, and implicating and supervising all stakeholders in the apps’ development process.

The UP2DATE H2020 project focuses on highperformance heterogeneous embedded platforms for critical systems. We will develop observability and controllability solutions to support online updates while ensuring safety and security for mixed-criticality tasks. In this paper, we describe the rationale behind the selection of the baseline research platforms which will be used to develop and demonstrate the project concepts, including a performance comparison to identify the most efficient one. This work is funded by the European Commission’s Horizon 2020 programme under the UP2DATE project (grant agreement 871465). It is also partially supported by the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB and FJCI-2017-34095 and HiPEAC.

Introducing advanced functionalities in safety-critical systems requires using more powerful architectures such as GPUs. However software in safety-critical industries is subject to functional certification, which cannot be achieved using standard GPU programming languages such as CUDA and OpenCL. Fortunately, GPUs are already used in certified critical systems for display tasks, using safety-certified solutions such as OpenGL SC 2.0. In this paper, we compare two state-of-the art graphics-based methodologies, OpenGL SC 2.0 and Brook Auto/BRASIL for the implementation of a prototype avionics case study. We evaluate both methods on a realistic industrial setup, composed by an avionics-grade GPU and a safety-certified GPU driver in terms of development metrics and performance, showing their feasibility. This work was funded by the Airbus TANIA-GPU Project ADS (E/200). It was also partially supported by the Spanish Ministry of Economy and Competitiveness under grants PID2019-107255GB and FJCI-2017-34095 and HiPEAC.

Context: Exploiting software development related data from software-development intensive organizations to support tactical and strategic decision making is a challenge. Combining data-driven approaches with expert knowledge has been highlighted as a sensible approach for leading software-development intensive organizations to rightful decision-making improvements. However, most of the existing proposals lack of important aspects that hinders their industrial uptake such as: customization guidelines to fit the proposals to other contexts and/or automatic or semi-automatic data collection support for putting them forward in a real organization. As a result, existing proposals are rarely used in the industrial context. Objective: Support software-development intensive organizations with guidance and tools for exploiting software development related data and expert knowledge to improve their decision making. Method: We have developed a novel method called SESSI (Specification and Estimation of Software Strategic Indicators) that was articulated from industrial experiences with Nokia, Bittium, Softeam and iTTi in the context of Q-Rapids European project following a design science approach. As part of the industrial summative evaluation, we performed the first case study focused on the application of the method. Results: We detail the phases and steps of the SESSI method and illustrate its application in the development of ModelioNG, a software product of Modeliosoft development firm. Conclusion: The application of the SESSI method in the context of ModelioNG case study has provided us with useful feedback to improve the method and has evidenced that applying the method was feasible in this context. This work was supported by the Spanish Ministry of Economy and Competitiveness (project GENESIS, grant agreement TIN2016-79269- R), and by the European Union’s Horizon 2020 research and innovation program (Q-Rapids project, grant agreement N◦732253).

Graphs play an important role in many areas of Computer Science. In particular, our work is motivated by model-driven software development and by graph databases. For this reason, it is very important to have the means to express and to reason about the properties that a given graph may satisfy. With this aim, in this paper we present a visual logic that allows us to describe graph properties, including navigational properties, i.e., properties about the paths in a graph. The logic is equipped with a deductive tableau method that we have proved to be sound and complete. This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Union (FEDER funds) under grant GRAMM (ref. TIN2017-86727-C2-1-R, TIN2017-86727-C2-2-R) and by the Basque Project GIU15/30, and grant UFI11/45.

Managing software projects gets more and more complicated with an increasing project and product size. To cope with this complexity, many organizations use issue tracking systems, where tasks, bugs, and requirements are stored as issues. Unfortunately, managing software projects might remain chaotic even when using issue trackers. Particularly for long lasting projects with a large number of issues and links between them, it is often hard to maintain an overview of the dependencies, especially when dozens of new issues get reported every day. We present a Jira plug-in that supports developers, project managers, and product owners in managing and overviewing issues and their dependencies. Our tool visualizes the issue links, helps to find missing or unknown links between issues, and detects inconsistencies. The work presented in this paper has been conducted within the scope of the Horizon 2020 project OpenReq, which is supported by the European Union under the Grant Nr. 732463.

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.

UML sequence diagrams are used to graphically describe the message interactions between the objects participating in a certain scenario. Combined fragments extend the basic functionality of UML sequence diagrams with control structures, such as sequences, alternatives, iterations, or parallels. In this paper, we present a UML profile to annotate sequence diagrams with combined fragments to model timed Web services with distributed resources under the publish/subscribe paradigm. This profile is exploited to automatically obtain a representation of the system based on Colored Petri nets using a novel model-to-model (M2M) transformation. This M2M transformation has been specified using QVT and has been integrated in a new add-on extending a state-of-the-art UML modeling tool. Generated Petri nets can be immediately used in well-known Petri net software, such as CPN Tools, to analyze the system behavior. Hence, our model-to-model transformation tool allows for simulating the system and finding design errors in early stages of system development, which enables us to fix them at these early phases and thus potentially saving development costs.

Context:Quality requirements (QRs) describe the desired quality of software, and they play an important role in the success of software projects. In agile software development (ASD), QRs are often ill-defined and not well addressed due to the focus on quickly delivering functionality. Rapid software development (RSD) approaches (e.g., continuous delivery and continuous deployment), which shorten delivery times, are more prone to neglect QRs. Despite the significance of QRs in both ASD and RSD, there is limited synthesized knowledge on their management in those approaches. Objective:This study aims to synthesize state-of-the-art knowledge about QR management in ASD and RSD, focusing on three aspects: bibliometric, strategies, and challenges. Research method:Using a systematic mapping study with a snowballing search strategy, we identified and structured the literature on QR management in ASD and RSD. Check the PDF file to see the full abstract and document., Comment: 31 pages, the article is currently in press in Information and Software Technology journal and can be accessed through https://doi.org/10.1016/j.infsof.2019.106225

Any software development process needs to consider non-functional requirements (NFR) in order to deliver a system that complies with its stakeholders' expectations. In a previous mapping study about model-driven development (MDD) for service-oriented architectures (SOA) we found a limited number of approaches managing NFR. The present work aims at analyzing in detail the state of the art in the management of NFR in MDD processes which produce SOA. We have conducted a systematic literature review following a rigorous protocol. We have taken as initial point the mapping study mentioned above and have used the subset of the 31 papers from this study (clustered into 15 approaches) that referred to NFR. We have analyzed them qualitatively in order to answer six research questions. We have built a Software Engineering theory to formalize this analysis. As result we highlight that most of approaches focus exclusively on security and reliability and we observe that NFR are expressed mainly as annotations of functional models represented in UML. From our perspective, existing research on the topic of this study is still scarce and without any evidence of transferability to industry. This situation suggests the need for further investigation efforts in order to produce validated MDD methods capable of generating SOA satisfying NFR stated by stakeholders.

Smart grids play an important role in the modernization and optimization of the existing electrical grid, to accomplish the current European Union Energy and Climate targets. Smart grids require distributed applications to manage the grid more efficiently. The performance of the distributed applications impacts on the communications delay time and on the timely interaction with the devices located in the users’ Home Area Networks. This paper presents the results of the ENCOURAGE project related to the development of a software platform to support smart grids. The work presented in this paper assesses four different middleware configurations and analyses the results on the delay performance tests. The results show that the mean end-to-end delay is between 310 ms and 453 ms in proper conditions. In terms of operational costs, the optimal configuration enables managing houses with less than 0.25 Euros per month per house. This paper justifies the maturity of the technology to support smart grids, and the possibility to transfer the ENCOURAGE project results to the industry.

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).

Background: During Rapid Software Development, a large amount of project and development data can be collected from different and heterogeneous data sources. Aims: Design a methodology to process these data and turn it into relevant strategic indicators to help companies make meaningful decisions. Method: We adapt an existing methodology to create and estimate strategic indicators using Bayesian Networks in the context of Rapid Software Development, and applied it to a use case. Results: Applying the methodology in the use case, we create a model to predict product quality based on software factors and metrics, using companies’ business knowledge and collected data. Conclusions: We proved the methodology’s feasibility and obtained positive feedback from the company’s use case.

We define a method for simplifying UML schemas to improve efficiency of reasoning.The method deals with schemas with general 1st-order constraints (i.e. most OCL).The method can be incorporated in any UML/OCL reasoning tool.Experiments show time improvements from x2 to x370 in different tools/schemas. Ensuring the correctness of a conceptual schema is an essential task in order to avoid the propagation of errors during software development. The kind of reasoning required to perform such task is known to be exponential for UML class diagrams alone and even harder when considering OCL constraints. Motivated by this issue, we propose an innovative method aimed at removing constraints and other UML elements of the schema to obtain a simplified one that preserve the same reasoning outcomes. In this way, we can reason about the correctness of the initial artifact by reasoning on a simplified version of it. Thus, the efficiency of the reasoning process is significantly improved. In addition, since our method is independent from the reasoning engine used, any reasoning method may benefit from it.

Strategic decision making is the process of selecting a logical and informed choice from the alternative options based on key strategic indicators determining the success of a specific organization strategy. To support this process and provide a common underlying language, in this work, we present an empirically-grounded ontology to support different strategic decision-making processes and extend the ontology to cover the context of managing quality in Rapid Software Development projects. We illustrate the complete ontology with an example.

Non-functional requirements (NFRs) are determinant for the success of software projects. However,they are characterized as hard to define, and in agile software development(ASD), are often given less priority and usually not documented. In this paper, we present the findings of the documentation practices and challenges of NFRs in companies utilizing ASD and propose guidelines for enhancing NFRs documentation in ASD. We interviewed practitioners from four companies and identified that epics, features, user stories, acceptance criteria,Definition of Done(DoD), product and sprint backlogs are used for documenting NFRS. Please refer to the manuscript for the full abstract., Comment: 8 pages, Authors post print version, the original work is published in Product-Focused Software Process Improvement, PROFES 2017

Executable models play a key role in many software development methods by facilitating the (semi)automatic implementation/execution of the software system under development. This is possible because executable models promote a complete and fine-grained specification of the system behaviour. In this context, where models are the basis of the whole development process, the quality of the models has a high impact on the final quality of software systems derived from them. Therefore, the existence of methods to verify the correctness of executable models is crucial. Otherwise, the quality of the executable models (and in turn the quality of the final system generated from them) will be compromised. In this paper a lightweight and static verification method to assess the correctness of executable models is proposed. This method allows us to check whether the operations defined as part of the behavioural model are able to be executed without breaking the integrity of the structural model and returns a meaningful feedback that helps repairing the detected inconsistencies. HighlightsChecking the quality of a model guarantees the quality of the final software system.Lightweight and static verifier to check the correctness of executable models.We focus on executability property of operations.Our method returns valuable feedback.

In this paper we present a new method for Joint Feature Selection and Classifier Learning using a sparse Bayesian approach. These tasks are performed by optimizing a global loss function that includes a term associated with the empirical loss and another one representing a feature selection and regularization constraint on the parameters. To minimize this function we use a recently proposed technique, the Boosted Lasso algorithm, that follows the regularization path of the empirical risk associated with our loss function. We develop the algorithm for a well known non-parametrical classification method, the relevance vector machine, and perform experiments using a synthetic data set and three databases from the UCI Machine Learning Repository. The results show that our method is able to select the relevant features, increasing in some cases the classification accuracy when feature selection is performed.

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.

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.

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Software requirement patterns have been proposed as a type of artifact for fostering requirements reuse. In this paper, we present PABRE-Proj, a tool aimed at supporting requirements elicitation and specification.

It is well known that requirements changes in a later phase of software developments is a major source of software defects and costs. Thus, the need of techniques to control or reduce the amount of changes during software development projects. The authors advocate the use of foresight methods as a valuable input to requirements elicitation, with the potential to decrease the number of changes that would be required after deployment, by anticipating them. In this paper, the authors define a process for using a foresight method, namely Futures Wheel, for requirements elicitation. To illustrate the use of this approach, the authors perform a case study using a route planning system.

Gestión de los equipos asignados a un departamento/empresa. Management teams assigned to a department / company. Gestió dels equips assignats a un departament / empresa.

Service-based systems need to provide flexibility to adapt both to evolving requirements from multiple, often conflicting, ephemeral and unknown stakeholders, as well as to changes in the runtime behavior of their component services. Goal-oriented models allow representing the requirements of the system whilst keeping information about alternatives. We present the MAESoS approach which uses i* diagrams to identify quality of service requirements over services. The alternatives are extracted and kept in a variability model. A monitoring infrastructure identifies changes in runtime behavior that can propagate up to the level of stakeholder goals and trigger the required adaptations. We illustrate the approach with a scenario of use.

PhD programs and graduate studies in robotics usually include motion planning among its main subjects. Students that focus their research in this subject find themselves trapped in the necessity of programming an environment where to test and validate their theoretic contributions. The programming of this robot motion planning environment is a big challenge. It requires on the one hand good programming skills involving the use of software development tools, programming paradigms, or the knowledge of computational complexity and efficiency issues. On the other hand it requires coping with different related issues like the modeling of objects, computational geometry problems and graphical representations and interfaces. The mastering of all these techniques is good for the curricula of roboticists with a motion planning profile. Nevertheless, the time and effort devoted to this end must remain reasonable. Within this framework, the aim of this paper is to provide the students with a roadmap to help them in the development of the software tools needed to test and validate their robot motion planners. The proposals are made within the scope of multi-platform open source code., Los programas de doctorado y los estudios de postgrado en robótica suelen incluir la planificación del movimiento entre sus temas principales. Los estudiantes que centran su investigación en este tema se ven atrapados en la necesidad de programar un entorno donde probar y validar sus aportaciones teóricas. La programación de este entorno de planificación del movimiento del robot es un gran reto. Requiere, por un lado, buenas habilidades de programación que impliquen el uso de herramientas de desarrollo de software, paradigmas de programación o el conocimiento de cuestiones de complejidad y eficiencia computacional. Por otro lado, requiere hacer frente a diferentes cuestiones relacionadas, como el modelado de objetos, los problemas de geometría computacional y las representaciones e interfaces gráficas. El dominio de todas estas técnicas es bueno para los currículos de los roboticistas con un perfil de planificación del movimiento. Sin embargo, el tiempo y el esfuerzo dedicados a este fin deben seguir siendo razonables. En este marco, el objetivo de este artículo es proporcionar a los estudiantes una hoja de ruta que les ayude en el desarrollo de las herramientas de software necesarias para probar y validar sus planificadores de movimiento robótico. Las propuestas se realizan en el ámbito del código fuente abierto multiplataforma.

In this paper, we present a software architecture and a development environment for the implementation of applications based on secure mobile agents. Recent breakthroughs in mobile agent security have unblocked this technology, but there is still one important issue to overcome: the complexity of programming applications using these security solutions. Our proposal aims to facilitate and speed up the process of implementing cryptographic protocols, and to allow the reuse of these protocols for the development of secure mobile agents. As a result, the proposed architecture and development environment promote the use of mobile agent technology for the implementation of secure distributed applications. © 2009 Elsevier Inc. All rights reserved., This work is partially supported by the Spanish Ministry of Science and Innovation and the FEDER funds under the Grants TSI2006-03481 SCRISSI , E-AEGIS TSI2007-65406-C03-02 and TSI2007-65406-C03-03 , CONSOLIDER-INGENIO 2010 CSD2007-00004 ARES . This work has also been funded by the Catalan AGAUR Grant 2009SGR1224

Currently, information systems are mainly built by integrating or customizing commercial off-the-shelf (COTS) components acquired or licensed from the marketplace. The processes necessary to steer a suitable acquisition are different from traditional software development processes. Among them, we are interested in the process of selection of COTS components. COTS selection requires discipline to coordinate the selection team and the set of new activities that are necessary to support a successful selection. The Capability Maturity Model (CMM) contains important guidelines for process improvement, and specifies "what" we must have into account to achieve the disciplined processes (among others things). On the other hand, agile methods are playing nowadays an important role in software engineering practices, because they are specifying "how" the software practices must be addressed to attain agility and improvement in the software processes. The contribution of this work is to propose a framework to reconcile agile and discipline-based approaches in the COTS selection domain, by including agile practices into the 5 levels of CMMI for COTS acquisition

Peer-reviewed In the statistical pattern recognition eld the number of samples to train a classifer is usually insu cient. Nevertheless, it has been shown that some learning domains can be divided in a set of related tasks, that can be simultaneously trained sharing information among the different tasks. This methodology is known as the multi-task learning paradigm. In this paper we propose a multi-task probabilistic logistic regression model and develop a learning algorithm based in this framework, which can deal with the small sample size problem. Our experiments performed in two independent databases from the UCI and a multi-task face classification experiment show the improved accuracies of the multi-task learning approach with respect to the single task approach when using the same probabilistic model.

The use of Commercial Off-The-Shelf (COTS) components is becoming a strategic need because they offer the possibility to build systems at reduced costs and within shorter development time. Having efficient and reliable COTS components selection methods is a key issue not only for exploiting the potential benefits of this technology, but also for facing the problems and risks involved. Searching COTS components requires to overcome several obstacles: the growing size and evolvability of the COTS marketplace, the dependencies from the components to be selected with others, and the type of descriptions currently available for those components. In this paper, we present a goal-oriented strategy for an effective localization, analysis and structuring of COTS components information. Our proposal is the GOThIC method, which provides methodological support to the construction of taxonomies. We present the seven activities that conform this method, which are illustrated with the case of real-time synchronous communication tools.

A method for synthesizing code for the software component of a system is proposed. The specification is given as a set of concurrent processes that communicate through channels. Each process is a sequential program that may contain data-dependent control statements. The synthesized software consists of a set of tasks. A task is generated by analyzing the computation associated to the occurrence of an event at each input port connected to the environment. Our task generation and scheduling algorithm guarantees that task execution can be performed with a finite amount of inter-task buffer memory under arbitrary input streams. Petri nets are used as the underlying model to formally analyze our algorithms. This model is also used to derive several algorithms for optimizing code generation for the set of tasks yielding compact and high-performance software implementations.

This paper presents NoFun, a notation aimed at dealing with non-functional aspects of software systems at the product level in the component programming framework. NoFun can be used to define hierarchies of non-functional attributes, which can be bound to individual software components, libraries of components or (sets of) software systems. Non-functional attributes can be defined in several ways, being possible to choose a particular definition in a concrete context. Also, NoFun allows to state the values of the attributes in component implementations, and to formulate non-functional requirements over component implementations. The notation is complemented with an algorithm able to select the best implementation of components (with respect to their non-functional characteristics) in their context of use.

The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it’s declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software’s release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.

Rapid software development (RSD) is an approach for developing software in rapid iterations. One of the critical success factors of an RSD project is to deliver the product releases on time and with the planned features. In this paper, we elaborate an exploratory definition of the On-Time Delivery strategic indicator in RSD based on the literature and interviews with four companies. This indicator supports decision-makers to detect development problems in order to avoid delays and to estimate the additional time needed when requirements, and specifically quality requirements, are considered.

Software requirement patterns have been proposed as an artifact for fostering requirements reuse. In this paper we present PABRE-Man, a software subsystem aimed at managing a catalogue of patterns ready to be applied in requirements engineering projects.

Agile methods are proposed nowadays as a way to support software systems procurement. Most of the existing proposals such as eXtreme programming or scrum seem to conceive software procurement as an exercise of software development. However, a great deal of software systems are commercial off-the-shelf (COTS)-based systems, in which the focus changes from bespoke software development to COTS selection and integration. Many proposals for COTS selection have been issued and therefore one may wonder how do they behave from the agile point of view. In this paper, we study the agile principles in the context of COTS selection and we analyze some of the most widespread existing methods. As a result, we identify some practices that would help in making COTS selection processes more agile.

The use of software analytics in software development companies has grown in the last years. Still, there is little support for such companies to obtain integrated insightful and actionable information at the right time. This research aims at exploring the integration of runtime and development data to analyze to what extent external quality is related to internal quality based on real project data. Over the course of more than three months, we collected and analyzed data of a software product following the CRISP-DM process. We studied the integration possibilities between runtime and development data, and implemented two integrations. The number of bugs found in code has a weak positive correlation with code quality measures and a moderate negative correlation with the number of rule violations found. Other types of correlations require more data cleaning and higher quality data for their exploration. During our study, several challenges to exploit data gathered both at runtime and during development were encountered. Lessons learned from integrating external and internal data in software projects may be useful for practitioners and researchers alike.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Companies in a software ecosystem must understand which power capabilities drive cooperation or generate conflicts. In this article, we analyze how power influences the relationships among companies in ecosystems formed by small-to-medium enterprises as well as in platform ecosystems governed by large keystones.

Data science is mandatory in today's business to capitalize on achievements and assets. This specifically holds for modern software development, where data science facilitates analyzing product, process, and usage and thus managing evolution and performance. With the convergence of embedded and IT domains, such as the Internet of Things (IoT) and automotive systems, software systems are becoming more complex. Complexity has two faces. On one hand it means more functionality and fluid delivery models, thus offering markets more value, such as the ability to deliver a single-customer focus. Complexity, however, also means the growth of technical debt, which slows productivity and lowers quality. As software engineering generates ever larger and more varied data sets, such as feature usage, code analysis, test coverage, error logs, and maintenance data, companies face the challenge of unlocking the value of that data.