Abstract Success in software projects is now an important challenge. The main focus of the engineering community is to predict software defects based on the history of classes and other code elements. However, these software defect prediction techniques are effective only as long as there is enough data to train the prediction model. To mitigate this problem, cross-project defect prediction is used. The purpose of this research investigation is twofold: first, to replicate the experiments in the original paper proposal, and second, to investigate other settings regarding defect prediction with the aim of providing new insights and results regarding the best approach. In this study, three composite algorithms, namely AvgVoting, MaxVoting and Bagging are used. These algorithms integrate multiple machine classifiers to improve cross-project defect prediction. The experiments use pre-processed methods (normalization and standardization) and also feature selection. The results of the replicated experiments confirm the original findings when using raw data for all three methods. When normalization is applied, better results than in the original paper are obtained. Even better results are obtained when feature selection is used. In the original paper, the MaxVoting approach shows the best performance in terms of the F-measure, and BaggingJ48 shows the best performance in terms of cost-effectiveness. The same results in terms of F-measure were obtained in the current experiments: best MaxVoting, followed by AvgVoting and then by BaggingJ48. Our results emphasize the previously obtained outcome; the original study is confirmed when using raw data. Moreover, we obtained better results when using preprocessing and feature selection.

The quality of software systems is an important aspect, especially for embedded systems, thus strategies and actions for analyzing the trade-off between various quality attributes need to be improved. Objectives: We target firstly to determine which quality attributes are important in embedded systems, and secondly to inquire about maintainability in particular, emphasizing the practices that are associated with it, i.e., coding rules, conventions, documentation, code review, and refactoring. Method: We used interviews and surveys as means to investigate practitioners’ points of view and practices. Applying quantitative and qualitative analysis, we explored a general perspective of quality attributes in embedded systems, followed by specific practices related to the maintainability attribute. Results: At the general perspective level, we learned that the importance of security and safety is extended to all embedded systems, while maintainability remains of major importance, and there is a diversity of methods used to assure the quality of systems throughout the development cycle . At the maintainability-specific level, we learned that code review and refactoring are the most used practices and that the related activities are performed in a variety of ways. Conclusions: Our work recognizes various quality attributes as being important with different priorities, respectively analyses which maintainability-related activities are used.

Reliability is one of the most important quality attributes of a software system, addressing the system’s ability to perform the required functionalities under stated conditions, for a stated period of time. Nowadays, a system failure could threaten the safety of human life. Thus, assessing reliability became one of the software engineering‘s holy grails. Our approach wants to establish based on what project’s characteristics we obtain the best bug-oriented reliability prediction model. The pillars on which we base our approach are the metric introduced to estimate one aspect of reliability using bugs, and the Chidamber and Kemerer (CK) metrics to assess reliability in the early stages of development. The methodology used for prediction is a feed-forward neural network with back-propagation learning. Five different projects are used to validate the proposed approach for reliability prediction. The results indicate that CK metrics are promising in predicting reliability using a neural network model. The experiments also analyze if the type of project used in the development of the prediction model influences the quality of the prediction. As a result of the operated experiments using both within-project and cross-project validation, the best prediction model was obtained using PDE (PlugIn characteristic) for MY project (Task characteristic).

As the world changes, so does the future of our students. In this respect, the evolution of the technology comes up with specific environments for educational purpose. Building smart learning environments supported by e-learning platforms is an important area of research in education domain within our days. The evolution of these smart learning environments is justified by some events (Covid19) that force students to learn remotely. The paper proposes a formalisation using ontology for providing an inclusive approach of universities’ websites, having as instance a software application component using Alexa smart speaker, that currently remains at a design level, which integrates different services (Amazon Web Services, Microsoft Services) for a proper virtual environment platform, for both students and teachers. It addresses the main concerns of the current educational system and provides a smart solution through the use of Artificial Intelligence based tools. The proposed approach not only achieves unifying data and knowledge-share mechanisms in a remotely mode, but it brings also a good learning experience, increasing the effectiveness and the efficiency of the learning process.

This Research to Practice Full Paper delineates the impact of using cyclic learning to obtain competences in software quality oriented design. Nowadays, the need for quality in the software systems has become more and more a concern for many researchers and industry practitioners. Developing students' appropriate competencies and skills in writing quality programs must be an important objective of any Software Engineering related course from the Computer Science Curricula. In order to attain this goal, the paper presents a new strategy for reflecting software quality models into Software Engineering related courses based on cyclic learning. The method is based on an educational strategy that integrates the cyclic learning approach and induces to the students the awareness regarding the importance of developing quality software. We focus on a set of software quality characteristics described by the ISO25010 quality model for which we analyze the level of knowledge attained by the students during the entire bachelor cycle of studies. The study is directed by a detailed analysis of three courses: Advanced Programming Methods, Parallel and Distributed Programming, and Software Systems Verification and Validation, which were chosen in order to master the analysis complexity, but at the same time to assure coverage of as many quality attributes as possible. The investigation includes qualitative and quantitative analysis, directed by the objective of establishing the efficiency and effectiveness of the approach. The results obtained confirm both students' awareness regarding the importance of learning software quality attributes, and the efficiency of using cyclic learning in teaching this subject. We also outline several insights and advantages, and we conclude by showing that the proposed strategy fulfilled the expected objectives.

This Research to Practice Full Paper proposes a novel incremental approach in teaching a Software Engineering related course, underpinned by active-learning methods. Teaching a Software Engineering related course for undergraduate students is a challenging task due to extremely frequent changes that appeared in programming paradigms and in software process development methodologies. In this everchanging domain, which presents us with opportunities and challenges every single day, it might be difficult and almost impossible for teachers to predict what are those knowledge that will be of use to students, on the long term, maybe even for five years from now. This rapid growth in the software development evolution has led to a gap between how teachers do knowledge transfer to students and how students acquire these knowledge. To bridge this gap there is a need to change how we teach these subjects. Thus, the new theory of education suggests that learner should be in the center of the learning process and the instructors playing an advising and facilitating role. A shift in education theory to a more student-centered approach using active learning is recommended because this approach has its own role to make the students creative and competent in their study. In this respect, active learning's main drive is to put the responsibility of learning at the hands of the learners themselves and to delegate the role of facilitator to the teacher. In this paper we provide novel approaches in teaching an undergraduate Software Engineering related course at the Babeş-Bolyai University. Its contribution is threefold: firstly, we introduce a new learning process design which selects active learning methods intertwining in teaching this course; secondly, we aim to study the impact of applying active learning methods on students' grades over the three academic years. Thirdly, we investigate students perceptions, their feedback and learning experiences on the use of applying active learning methods. Also, the paper reviews challenges and constraints that we faced while trying to teach this course. The analysis results show the effectiveness of our approach. Also, our students have expressed a high level of satisfaction, and in a survey, they indicated that the skills they learned in the course are highly applicable to their careers, facilitating their interviews for the engagement within IT companies.

Regression testing is used every time a change is taking place in the source code, various approaching for the test cases to be executed focusing on different criteria: from the maximization of faults and/or code coverage to minimization of time execution. Test Case Prioritization is one of such approaches that aim to optimize the execution order of test cases according to various criteria. However, many regression testing approaches use only code coverage criteria, few considered requirements. This paper aims to propose a fuzzy clustering approach with various metrics of the considered test cases, considering several aspects: faults, execution time, requirements covered by the test cases, and requirements dependencies. An in-depth analysis will follow to determine what are the best metrics to be used in the TCP. This will have a positive impact on the research community by identifying new perspectives to be considered for the TCP.

The new theory of education suggests that learner should be in the center of the learning process and the instructor playing an advising and facilitating role. Building smart learning environments supported by e-learning platforms is an important area of research. The rapid and continuous development of technology that has brought new learning skills for students forces the educational system to enter into a new era. This change is further justified by some unprecedented events that force students to learn remotely. QLearn is an e-learning platform developed as a web based application which provides quizzes for students enrolled at Advanced Programming Methods course from Babeş Bolyai University (Romania) as part of their formative and summative assessment. The existing set of quizzes has been proposed by students throughout the last three iterations of the course in a collaborative manner with course instructor, and this data set is expanding continuously with every new generation of students. QLearn is a smart learning environment offering the students valuable feedback and a good preparation for the exam. Some metrics that quantify the coverage rate of the course syllabus attained by students or their understanding level of knowledge are provided by QLearn. The Artificial Intelligent component of QLearn application uses these measures to make predictions for students' outcomes at the exam, to find out which topics need to be practised more and to recommend learning plans according to students' individual needs. The contribution of the paper is therefore twofold. Firstly, we propose a new learning design, based on students' involvement in a collaborative manner. The second contribution of the paper is QLearn, a software application that provides support (implementation) for the proposed learning process design. Not only does QLearn platform provide a smart learning environment for students, but it also ensures the knowledge transfer from instructors to students in an efficient and effective way.

Reliability ensures architectural strength and error free operations of software systems. Software design and architectural measures have been studied as key indicators of faults in software systems. However, association between quantification of reliability prediction using design-level metrics has not been explored. This paper presents a novel approach of developing reliability metrics and it’s prediction using package level metrics. In particular, relevant fault severity information is empirically experimented with package level metrics in an effort-aware classification and ranking scenario. Results obtained hint significant view to predict the reliability of software systems using architectural level metrics. Therefore, the empirical analysis can guide development process to be design-focused and avoid accumulation of faults in implementation phase.

Looking back to the rumours from the early 2000's, when the world of technology bloomed together with the curiosity towards what was next to come, by 2020, robots should have assisted and supported almost every task from our daily life. While this may seem as a Sci-Fi movie scenario, it is partially a tangible reality, that we quickly got used to, thanks to the introduction of smart speakers. As the world changes, so does the future of our students. In this respects, the evolution of the technology comes up with specific environments for educational purpose. Building smart learning environments supported by e-learning platforms is an important area of research in education domain within our days. The evolution of these smart learning environments is justified by some events (Covid19) that force students to learn remotely. The paper proposes a software application component using Alexa smart speaker, that integrates different services (Amazon Web Services, Microsoft Services) for a proper virtual environment platform, for both students and teachers. It addresses the main concerns of the current educational system, and provides a smart solution through the use of Artificial Intelligence based tools. The proposed approach not only achieves unifying data and knowledge-share mechanisms in a remotely mode, but it brings also a good learning experience, increasing the effectiveness and the efficiency of the learning process.

This Research to Practice Ful1 Paper presents a study regarding applying cyclic learning strategy with a special focus on object-oriented programming and states our findings, emphasizing both the advantages and disadvantages.The research considers as a use-case the teaching activity in the Faculty of Computer Science of Babeş-Bolyai University. The analysis takes into consideration several disciplines that compass a set of interconnected teaching objectives and aspects: (1) fundamental concepts and mechanisms (F) defined by object-orientated programming paradigm, (2) design principles, heuristics, and rules (D) that act as strategies implied in object-oriented design, and (3) functional and nonfunctional requirements related to software architecture (A).The study is directed by a statistical analysis of the grades obtained by the students at different courses that treat the (F, D, A) aspects, and also the results obtained at the Bachelor's final exam that evaluates the level of the acquired fundamental knowledge. Their evolution and correlation during a period of several years are analyzed, and together with an analysis of the degree of absorption of the students in the IT industry form the base of the study.