Recognizing eye tracking traits for source code review

Computer programming skill is an essential skill for information technology and for some other natural science department students. However, teaching and learning computer programming ideas and skills has been recognized as a great challenge to both teachers and students. Therefore, the development of efficient learning strategies and environments for computer programming courses have become an important concept. This study is intended to recommend strategies for improving academic performance which can be used in teaching computer programming course effectively to undergraduate students in information technology related departments or informatics departments (including information Technology, computer science, and software engineering and information systems This concluded that there is a need for strategies for improving academic performance which requires planning and implementation to strengthen the motivation of the students to study, boost their academic performance, and uplift the quality of education in information technology departments. Finally, relevant and important workable recommendations have been suggested by the researcher.

Machine Learning has been extensively used in studies that utilized neurophysiological data (e.g., fMRI, EEG, ECG) in capturing brain and the Autonomic Nervous System (ANS) activity in various software engineering (SE) tasks such as code comprehension and development. However, there is a lack of research efforts to review machine learning techniques used in code comprehension and code review analysis as primary tasks of SE. This quick review partly covers the research body that used machine learning techniques in code comprehension and code review analysis using neurophysiological signals (i.e., neural and ANS activity) as their primary input feature. The analysis shows that more research is needed to investigate the potential of wearable biofeedback devices (i.e.., smart-watches that generate relevant data for machine learning methods) in capturing the ANS responses of software developers while performing intellectual and abstract tasks.

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

Welcome to the first edition of the International Workshop on Test Oracles (TORACLE 2021) to be held virtually on August 24, 2021, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Testing is an important activity in software engineering, especially with the growing adoption of software systems in safety-critical domains. Testing research, however, is mostly focused on determining which test inputs to use (e.g., proposing and evaluating test coverage criteria or automatic test generation tools). Regardless of the used coverage criterion, we need to know whether a given program executes correctly on a given input. Indeed, a test execution for which we cannot discriminate between success or failure is fruitless. This corresponds to the so-called ”oracle problem”, the problem of knowing whether a program behaves correctly for a specific input. While the importance of the oracle problem is well understood, only a few alternatives exist to manually deriving test oracles. This makes test oracle automation one of the main bottlenecks for full test automation. Therefore, novel approaches and tools are needed to address this important problem.

Good software engineering methodology can be rendered ineffective by the presence of deadline pressure. A simple model is developed to explain the possible responses to deadline pressure and to show that there is a tendency to divert resources away from software engineering activities under such conditions. Management is identified as being responsible for ensuring that software engineering activities are given adequate resources. A managerial strategy for achieving these ends is suggested.

Software Engineering is a knowledge-intensive activity, requiring extensive knowledge of the application domain and of the target software itself. Many Software Engineering costs can be attributed to the ineffectiveness of current techniques for managing this knowledge, and Artificial Intelligence techniques can help alleviate this situation. More than two decades of research have led to many significant theoretical results, but few demonstrations of practical utility. This is due in part to the amount and diversity of knowledge required by Software Engineering activities, and in part to the fact that much of the research has been narrowly focused, missing many issues that are of great practical importance. Important issues that remain to be addressed include the representation and use of domain knowledge and the representation of the design and implementation history of a software system. If solutions to these issues are found, and experiments in practical situations are successful, the implications for the practice of Software Engineering will be profound, and radically different software development paradigms will become possible.

Enterprise information systems(EIS) focus on dealing with the complex business logic of collecting, filtering, processing, and distributing data for improving productivity and service in our daily life. The successful development of enterprise information system is the labor-intensive activities in software engineering, it requires the sophisticated human efforts for requirements validation, system design, implementation and verification. Our previous work RM2PT can help to achieve a validated requirements model through automatically generating prototypes from requirements models to support incremental and rapid requirements validation. In this paper, we present a tool named RM2EIS to further alleviate the problem of system development by supporting automatically generate the back-end source code of enterprise information system from the validated requirements model, which are achieved by several round requirements validation in RM2PT. We demonstrate that RM2EIS can achieve higher quality code (+8.27%) with less time cost (-61.92%) than the manual development through 9 case studies. Overall, the results were satisfactory. The proposed approach can be further extended and applied for the EIS development in the software industry. The tool can be downloaded at https://rm2pt.com/advs/rm2eis and a demo video casting its features is available at https://www.youtube.com/watch?v=5Nde-JYezg4.

Defect reports are generated from various testing and development activities in software engineering. Sometimes two reports are submitted that describe the same problem, leading to duplicate reports. These reports are mostly written in structured natural language, and as such, it is hard to compare two reports for similarity with formal methods. In order to identify duplicates, we investigate using natural language processing (NLP) techniques to support the identification. A prototype tool is developed and evaluated in a case study analyzing defect reports at Sony Ericsson mobile communications. The evaluation shows that about 2/3 of the duplicates can possibly be found using the NLP techniques. Different variants of the techniques provide only minor result differences, indicating a robust technology. User testing shows that the overall attitude towards the technique is positive and that it has a growth potential.

Verification and validation are becoming the most important activities in software engineering because of the stronger need to ensure and enhance quality. The system designers need to ensure the conformance of models with the functionalities of a target system and the correctness, safeness and reliability of its operating cycles. Many formal methods have been promoted to support such tasks and Petri nets formalism seems to be one of the most relevant one to evaluate control properties. But it lacks of structuration facilities to allow to handle large scale systems. The aim of this paper is to show that, for some guidelines, Petri nets can be integrated in usual object-oriented software methodologies in order to evaluate such properties. Hence, one can profit from contributions of both object and nets to handle quality in systems in a more satisfactory way.KeywordsObject MethodologyPetri NetsControl PropertiesVerification & Validation

Software development effort estimation is one of the most crucial activities in software engineering. Effort estimation permits managers and software engineers to anticipate, forecast, and precisely quote the schedule, budget and manpower requirements. By accurately estimating the effort; software projects can be saved from under run or over run. In this paper we have summarized and then analyzed the past work of software effort estimation in a systematic way. 10 researches were surveyed and explained briefly that how they are contributing towards solving the effort estimation problem in terms of time, cost or test. It also emphasizes that various effort estimation models have different pros and cons and can be used in different context on basis of different types of historical data. The survey discovered the most popular models used for effort prediction are supervised learning algorithms. The trends identified through this survey can help in exploring the potential research areas.

The study of change impact is a fundamental activity in software engineering because it can be used to plan changes, set them up and to predict or detect their effects on the system and try to reduce them. Various methods have been presented in the literature for this sector of maintenance. The objective of this project is to improve the maintenance of Object Oriented (OO) systems and to intervene more specifically in the task of analyzing and predicting the change impact. Among several models of representation, Bayesian Networks (BNs) constitute a particular quantitative approach that can integrate uncertainty in reasoning and offering explanations close to reality. Furthermore, with the BNs, it is also possible to use expert judgments to anticipate the predictions, about the change impact in our case. In this paper, we propose a probabilistic approach to determine the change impact in OO systems. This prediction is given in a form of probability.

With the tremendous growth in the complexity of software development in the last 1015 years, the management of risks in software engineering activities is becoming an important and non-trivial issue from three perspectives: project, process and product. Therefore, researchers and practitioners are continually trying to find effective risk management approaches. In this paper, we briefly discuss the principles of risk management in software engineering, review some of the process models popular in the software engineering community, and finally discuss some of the important works conducted recently in this area in the last five years. While writing the paper, we have focused mostly on project, and process-based risks and not on product risks.

Human interaction and behavior are at the core of most software engineering (SE) activities. Furthermore, software is created to fulfill human stakeholders' needs and wishes, and the resulting software is ultimately utilized (directly or indirectly) by human users. Today's software is highly intertwined with our lives, and it possesses an increasing ability to act and influence us. Besides the obvious example of self-driving cars and their potential harmfulness, more mundane software such as social networks may introduce bias, break privacy preferences, lead to digital addiction, etc. Additionally, the SE process itself is highly affected by ethical issues, such as diversity and business ethics. This paper introduces ethics-aware SE, a version of SE in which the ethical values of the stakeholders (including developers and users) are captured, analyzed, and reflected in software specifications and in the SE processes.

Model-Driven Engineering (MDE) is changing the way we build, operate, and maintain our software-intensive systems. Several projects using MDE practices are reporting significant improvements in quality and performance but, to be able to handle these projects, software engineers need a set of technical and interpersonal skills that are currently not widely available. The MDE postgraduate diploma intends to fill this gap by offering a full-time one year formation onMDE. The course syllabus is designed to teach students how to work at a higher abstraction level by the rigorous use of (software) models as the main artifacts in all software engineering activities. Contents include the conceptual framework of MDE plus all techniques and tools (e.g. for defining modeling languages, models, and model transformations) required to successfully complete software engineering projects following a MDE approach. The organizational impact (and challenges) of adopting MDE are exemplified using real industrial experiences. This article describes the MDE Diploma and the lessons that we learned after completing its first edition.

Decision-making is a central activity in agile software engineering (SE), yet research on how decisions are made and supported in agile contexts remains fragmented across models, methods, roles, and lifecycle stages. While prior studies have examined isolated aspects such as prioritization or planning, a comprehensive synthesis of decision-making as a phenomenon in agile SE is lacking. This systematic literature review addresses this gap by consolidating and structuring existing research on agile decision-making and to identify dominant patterns, gaps, and future research directions. A systematic search was conducted in IEEE Xplore, ACM Digital Library, Scopus, and Web of Science, complemented by backward and forward snowballing, covering publications from 2014 to 2024. In total, 42 studies were included and analyzed using a structured coding scheme covering decision models, methods, actors, lifecycle contexts, and research methodologies. The results reveal a strong concentration of analytical and hybrid decision-making models in planning and requirements activities, while decision-making in coding, testing, and operations remains underexplored. Software developers are the most frequently studied decision-making actors, whereas managers are mainly discussed as external stakeholders rather than active decision-makers within agile workflows. The main contributions of this study are the following: a structured synthesis of agile decision-making research over multiple analytical dimensions, the identification of key research gaps in lifecycle coverage and actor perspectives, and the proposal of a coherent nomenclature for decision-making in agile SE. These contributions provide a foundation for future empirical studies and support the development of more comprehensive theories of decision-making in agile software engineering organizations.