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Business Process Modeling is a skill that is becoming sought after for computer engineers, with Business Process Modeling Notation (BPMN) being one example of the tools used in modeling activities. Students of the Master of Computer Engineering course at Politecnico di Torino learn about BPMN in dedicated courses but often underperform on BPMN-related exercises due to difficulties understanding how to model processes. In recent years, there has been a surge of studies that employ gamification (using game elements in non-recreative contexts to obtain benefits) as a tool in Computer Engineering education to increase students' engagement with the learning process. This study aims to use the principles of gamification to design a supplementary learning tool for the teaching of information systems technology. In particular, to improve student understanding and use of BPMN diagrams. This study also analyzes the usability and motivation of the participants in using different game elements in increasing student motivation and performance. As part of the study, a prototype web application was developed, which implemented three different designs, each incorporating different game elements relating to either progress, competition, or rewards. An evaluation was then conducted on the prototype to evaluate the performance of the practitioners in performing BPMN modeling tasks with the gamified tool, the usability of the proposed mechanics and the enjoyment of the individual game mechanics that were implemented. With the usage of the gamified tool, the users of the experimental sample were able to complete BPMN modeling tasks with performances compatible with estimates made through expert judgement (i.e., gamification had no negative effect on performance), and were motivated to check the correctness of their models many times during the task execution. The system was evaluated as highly usable (85.8 System Usability Score); the most enjoyed game elements were rewards, levels, progress bars and aesthetics. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.05.077 Byline: Evgenia Egorova, Marco Torchiano, Maurizio Morisio Keywords: Software process; Project factors; Developers' perception; Survey; Case-control Study Abstract: A commonly cited limitation of software engineering research consists in its detachment from the industrial practice. Several studies have analyzed a number of practices and identified their benefits and drawbacks but little is known about their dissemination in the industry. For a set of 18 practices commonly studied in the literature, this paper investigated diffusion, effect on the success, and perceived usefulness in 62 actual industrial projects from 28 Italian IT companies. In particular we proposed a classification of these perceptions and we were able to classify 14 practices. We found statistical evidence that 7 factors have an actual effect (positive for 6 of them, negative for one). Moreover 77% (10 out of 13) of the known good practices (e.g., importance of good project schedule or complete requirements' list) are perceived consistently by the industry. For a few other practices (having a champion's support, using metrics, reducing quality) we noticed a lack of awareness in the industry. Starting from these observations we propose guidelines for industrial practice and suggestions for academic research. Author Affiliation: Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy Article History: Received 12 December 2009; Revised 5 May 2010; Accepted 6 May 2010

Android applications do not seem to be tested as thoroughly as desktop ones. In particular, graphical user interface (GUI) testing appears generally limited. Like web-based applications, mobile apps suffer from GUI test fragility, i.e., GUI test classes failing or needing updates due to even minor modifications in the GUI or in the application under test. The objective of our study is to estimate the adoption of GUI testing frameworks among Android open-source applications, the quantity of modifications needed to keep test classes up to date, and their amount due to GUI test fragility. We introduce a set of 21 metrics to measure the adoption of testing tools and the evolution of test classes and test methods, and to estimate the fragility of test suites. We computed our metrics for six GUI testing frameworks, none of which achieved a significant adoption among Android projects hosted on GitHub. When present, GUI test methods associated with the considered tools are modified often, and a relevant portion (70% on average) of those modifications is induced by GUI-related fragilities. On average, for the projects considered, more than 7% of the total modified lines of code between consecutive releases belong to test classes developed with the analyzed testing frameworks. The measured percentage was higher on average than the one required by other generic test code, based on the JUnit testing framework. Fragility of GUI tests constitutes a relevant concern, probably an obstacle for developers to adopt test automation. This first evaluation of the fragility of Android scripted GUI testing can constitute a benchmark for developers and testers leveraging the analyzed test tools and the basis for the definition of a taxonomy of fragility causes and guidelines to mitigate the issue. [ABSTRACT FROM AUTHOR]

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc. [ABSTRACT FROM AUTHOR]

The connected car—a vehicle capable of accessing to the Internet, of communicating with smart devices as well as other cars and road infrastructures, and of collecting real-time data from multiple sources—is likely to play a fundamental role in the foreseeable Internet Of Things. In a context ruled by very strong competitive forces, a significant amount of car manufacturers and software and hardware developers have already embraced the challenge of providing innovative solutions for new-generation vehicles. Today’s cars are asked to relieve drivers from the most stressful operations needed for driving, providing them with interesting and updated entertainment functions. In the meantime, they have to comply with the increasingly stringent standards about safety and reliability. The aim of this article is to provide an overview of the possibilities offered by connected functionalities on cars and the associated technological issues and problems, as well as to enumerate the currently available hardware and software solutions and their main features. [ABSTRACT FROM AUTHOR]

Recommender systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in RS that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data have been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations, use of more datasets considering the heterogeneity introduced, creation of new hybrid RS for adding information, definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets, and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A proposed method evaluates eight popular software reliability growth models and selects the one that can best predict the software's remaining faults, providing practical support for project managers who are considering an open source component. [ABSTRACT FROM PUBLISHER]

Most studies and regulatory controls focus on hardware-related measurement, analysis, and control for energy consumption. However, all forms of hardware include significant software components. Although software systems don't consume energy directly, they affect hardware utilization, leading to indirect energy consumption. Therefore, it's important to engineer software to optimize its energy consumption. The software engineering research domain has recently been paying attention to sustainability, as the increased number of publications, empirical studies, and conferences on the topic demonstrate. The guest editors of this special issue explain the articles they selected to highlight this important research area. The Web extra at http://youtu.be/h0tQoOH9_aM is an audio interview in which IEEE Software multimedia editor Davide Falessi interviews guest editor Ayse Basar Bener about how green software can help minimize the negative effects of software products and their development. [ABSTRACT FROM PUBLISHER]

Practitioners generally view exploratory testing (ET) as a cost-effective substitute for their daily testing activities. However, empirical evidence reported in the literature gives a more comprehensive picture of ET that considers its technical debt implications. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays saving energy is an interdisciplinary key challenge. Green IT deals with saving energy in IT systems, and is rapidly gaining momentum. Hardware manufacturers and designers have first considered the problem, in the field of IT, but recently software energy efficiency gathered the interest of industry and academic research. In this paper we aim at summarizing the available knowledge in Green IT. In particular we: [•] Introduce a taxonomy of concepts related to energy and IT. [•] Present recent data on energy consumption trends organized according to the taxonomy. [•] Present some guidelines to write energy efficient software organized according to the taxonomy. [•] Underline what is missing and what should be done in future research. [Copyright &y& Elsevier]

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Union. [ABSTRACT FROM AUTHOR]

The (semi-)automated management of programming assignments promises efficiency in large classes and fairness for the students. This paper presents an approach and the relative supporting environment for automated assessment of programming assignments that was developed for an object-oriented programming course. The user interface of the tool is tightly integrated into the Eclipse development environment, which is used by students in the labs. The assessment and grading are based on automated tests that allow an objective evaluation of the working features delivered by the students. The grading is based on the number of test eases passed, and on the distance, measured in lines of code, between the program developed in the lab and a corrected, fully functional version of it. This two-phased approach allows achieving a quantitative evaluation of the defects and of the missing functions. [ABSTRACT FROM AUTHOR]

The article presents ten facts regarding the selection and integration of commercial off-the-shelf (COTS) and open source software components in the development of software systems as practiced by industries. Facts mentioned include the traditional practice of using off-the-shelf (OTS) specific activities for integrating OTS components by integrators, the absence of formal selection procedures among integrators of OTS components and the lack of a specific development process phase which integrators may use in selecting OTS components.

Software entities (software products or processes) are characterized by many attributes, each one in its turn can be measured by one or more measures. In several cases the software entities have to be evaluated as a whole, thus raising the problem of aggregating measures to give an overall, single view on the software entity. This paper presents a method to aggregate measures, which works by comparing the product/process with predefined, ideal entities, or profiles. Profiles are defined starting from ranges of values on measures of attributes. The method is based on two main phases, namely definition of the evaluation model and application of the evaluation model. It is presented through a simplified case study that deals with evaluating the level of quality of an asset to decide whether to accept it in a reuse repository. A plausible way of how the method could be applied to process maturity assessment is also provided. The advantages of the method are that it allows using ordinal scales, while it deals explicitly with preferences expressed, implicitly or explicitly, by the evaluator. [ABSTRACT FROM AUTHOR]

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f 1 score of our model with different configurations and with a baseline classifier. We obtained an f 1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%. [ABSTRACT FROM AUTHOR]

Intelligent agents have the potential to understand personality traits of human beings because of their every day interaction with us. The assessment of our psychological traits is a useful tool when we require them to simulate empathy. Since the creation of social media platforms, numerous studies dealt with measuring personality traits by gathering users' information from their social media profiles. Real world applications showed how natural language processing combined with supervised machine learning algorithms are effective in this field. These applications have some limitations such as focusing on English text only and not considering polysemy in text. In this paper, we propose a multilingual model that handles polysemy by analyzing sentences as a semantic ensemble of interconnected words. The proposed approach processes Facebook posts from the myPersonality dataset and it turns them into a high-dimensional array of features, which are then exploited by a deep neural network architecture based on transformer to perform regression. We prove the effectiveness of our work by comparing the mean squared error of our model with existing baselines and the Kullback–Leibler divergence between the relative data distributions. We obtained state-of-the-art results in personality traits estimation from social media posts for all five personality traits. [ABSTRACT FROM AUTHOR]

Recommender systems have gained a lot of popularity due to their large adoption in various industries such as entertainment and tourism. Numerous research efforts have focused on formulating and advancing state-of-the-art of systems that recommend the right set of items to the right person. However, these recommender systems are hard to compare since the published evaluation results are computed on diverse datasets and obtained using different methodologies. In this paper, we researched and prototyped an offline evaluation framework called Sequeval that is designed to evaluate recommender systems capable of suggesting sequences of items. We provide a mathematical definition of such sequence-based recommenders, a methodology for performing their evaluation, and the implementation details of eight metrics. We report the lessons learned using this framework for assessing the performance of four baselines and two recommender systems based on Conditional Random Fields (CRF) and Recurrent Neural Networks (RNN), considering two different datasets. Sequeval is publicly available and it aims to become a focal point for researchers and practitioners when experimenting with sequence-based recommender systems, providing comparable and objective evaluation results. [ABSTRACT FROM AUTHOR]

We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey. [ABSTRACT FROM AUTHOR]

In recent years, the wireless Internet has become reality. Access is wireless through personal, local or global networks. Several applications are being and will be developed, either to adapt existing ones to the new infrastructure, or to support several mobile services. This article deals with engineering challenges at the application level. It presents the concept of context awareness and adaptation or the capability of an application to adapt to different terminals or to a changing geographical position. This theme is based on two research topics--namely, middleware for mobility and adaptation of the application to the terminal. Traditional middleware supports the communication among applications or components resident on a node. With mobility of terminals and components from node to node, new problems arise which extends middleware with new functions. The latter poses a hard problem because it is strictly linked with the semantics of the application. Effective adaptation requires working at the semantic level.

This article presents information related to comments on "More Success and Failure Factors in Software Reuse." The first and main observation of the data set is based not only on the data set itself but also on the knowledge gathered during the interviews with project members. The main problem with the data set is its size: 23 data points. Although this data set is the largest one available about reuse projects, it is too limited to base analysis only on data mining techniques; data mining is usually applied to data sets with thousands if not millions of data points.