330 research outputs found

    Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

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    Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

    A systematic literature review on the code smells datasets and validation mechanisms

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    The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of smell, number of smells, and the ratio of smelly to non-smelly samples in the dataset. Most existing datasets support God Class, Long Method, and Feature Envy while six smells in Fowler and Beck's catalog are not supported by any datasets. We conclude that existing datasets suffer from imbalanced samples, lack of supporting severity level, and restriction to Java language.Comment: 34 pages, 10 figures, 12 tables, Accepte

    Exploring the relationship among stress, psychological wellbeing, and performance in healthcare professionals and healthcare students

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    Background: The prevalence of high stress in healthcare professionals (HCPs) and healthcare students has gained immense attention over the past decade; and more so since the onset of the COVID-19 pandemic. High levels of stress have been shown to have adverse consequences on the psychological wellbeing, and aspects related to performance, in both HCPs and healthcare students, and particularly in the nursing sector. While prior quantitative research has demonstrated a significant relationship among stress, psychological wellbeing and work performance in HCPs worldwide, there is a need to better understand the factors entailed in this dynamic relationship, and gain more in-depth insight from a qualitative perspective too, particularly in nurses working for the National Health Service (NHS). Likewise, such factors warrant further exploration in nursing students too as they comprise a highly stressed population as compared to students in other fields due to the prevalence of complex and challenging issues that start to arise early in their education and training. Besides exploring the various stressors and factors associated with stress and other aspects of psychological wellbeing and performance, it is also important to look into the various coping strategies and resources that are employed in nursing staff and students within the context of stressful situations, including their uptake and views of available wellbeing courses or other sources of support offered within the healthcare settings. Aim: Following an Introduction (Chapter 1) to the key constructs and background pertinent to the current PhD project, the thesis presents a scoping review (Chapter 2) intended to look into psychological wellbeing intervention studies in NHS employees in terms of outcomes associated with improved psychological wellbeing and aspects related to work performance. The next chapter (Chapter 3) presents the core theoretical and practical aspects of the Methodology employed in the three studies which follow: Study 1 (Chapter 4) was intended to cast light into the relationship among stress, psychological wellbeing, aspects of work performance, and coping in nurses working for the NHS. Study 2 (Chapter 5) evaluated the effectiveness and acceptability of an eight-week mindfulness-based cognitive therapy programme (MBCT) delivered to NHS employees. Study 3 (Chapter 6) explored the relationship among perceived stress, coping strategies, emotional intelligence, and self-efficacy in UK nursing students. Finally, the findings across all studies and their implications are integrated in the General Discussion (Chapter 7) wherein an account of the project strengths and limitations is offered along with future directions for research and practice. Method: A mixed-method design was adopted across all three studies for data collection and analysis; quantitative data has been derived through validated self-report questionnaires via an online survey platform. Qualitative data has been collected through semi-structured interviews conducted via Microsoft Teams, including also a series of focus-groups in Study 3. Study 2 adopted a pre-/post- intervention, mixed-methods design, with quantitative assessments obtained at baseline (pre-intervention) and post-intervention and qualitative data obtained through interviews post intervention. Results: In study 1, regression analysis demonstrated a significant positive relationship between stress and impaired work performance; a significant negative relationship between stress and work satisfaction; a significant negative relationship between stress and overall work activity impairment; including a partially mediating role of emotional intelligence between stress and impaired work performance. Thematic analysis revealed the presence of various stressors pre and during the global pandemic, with workload being a major factor; impact of stress on several aspects of psychological wellbeing (in terms of low mood or depression, feelings of frustration, difficulty switching off, anxiety, lifestyle changes, and negative work-life balance); impact of stress on work performance (in terms of inefficient delivery of tasks, poor decision making skills, concentration difficulties, limited attention span, increased errors, forgetting important information, and feelings of frustration towards other colleagues); seeking social support as a major coping mechanism adopted by majority of nurses (from peers, seniors, professionals, and loved ones); receiving support from the organisation (including line managers) although some staff reported lack of adequate support. Uptake of certain wellbeing interventions was reported, but some staff reported lack of awareness and other barriers associated with engagement such as long waiting lists, lack of time, or simply not feeling the need to participate). With regard to study 2, Wilcoxon Signed Rank test findings revealed at post-intervention stage significant reductions in depression and stress, and a significant increase in levels of mindfulness and overall quality of life. Emerged themes reflected beneficial perceived changes in stress and other aspects of psychological wellbeing or state and perceived acceptability of the MBCT programme, while offering recommendations for improvement in future implementation. In study 3, a weak negative correlation was revealed between problem-focused coping and stress; a strong positive correlation between stress and avoidance coping; and no association was found between stress and emotion-focused coping. Emotional intelligence and self-efficacy had a positive significant correlation; a negative association was found between stress and emotional intelligence; and a strong negative correlation between stress and self-efficacy. Finally, a weak positive correlation was found between problem-focused coping and emotional intelligence; and between problem-focused coping and self-efficacy. Further, emotional intelligence did not moderate or mediate the relationship between self-efficacy and stress. Emerged themes highlighted various stressors experienced by nursing students, with balancing between academic and clinical placements being the major source of stress; the impact of stress on psychological wellbeing (in terms of low mood, feelings of demotivation, feelings of frustration with oneself and others around them, feeling overwhelmed difficulties switching off and experiencing low self-esteem); seeking social support as the most common coping strategy (from peers, teachers, university welfare services, professionals, and loved ones); and high perceived self-efficacy. Findings from focus-groups revealed a mixture of problem-focused and emotion-focused coping strategies when placed under stressful academic and placement situations. Conclusions: The combined pattern of quantitative and qualitative findings across all three studies has demonstrated high stress levels among HCPs and students, along with associated negative effects on psychological wellbeing and work or academic performance. However, it also placed emphasis on the significance of personal resources (EI, coping strategies, and self-efficacy) as well as job resources for improving one’s psychological wellbeing and aspects related to one’s work or academic performance. While stress is an inevitable aspect in healthcare settings, the NHS organisations and educational institutions should consider providing enhanced support for improving personal and organisational resources in healthcare staff and students in order to promote or improve their psychological wellbeing and performance

    Understanding, Analysis, and Handling of Software Architecture Erosion

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    Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance

    Internet and Biometric Web Based Business Management Decision Support

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    Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810

    A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit

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    Software quality assessment and prediction has been a research hotspot and has become even more critical in continuous software engineering. Modifications to a software product developed following a continuous software engineering process typically commence as a sequence of frequent commits, following a philosophy of “commit small, commit often.” Continuous integration (CI) and continuous deployment (CD) are essential concepts in this development environment. The challenge then is to develop techniques and tools which allow the development team to assess the overall quality posture of a software module in the period from a bug-inducing commit (i.e., when a bug is reported) to a bug-fixing commit (i.e. when a bug is reported fixed. The hypothesis is that in this period, the quality posture of the software modules involved in a bug-inducing/bug-fixing commit pair undergoes changes which may give developers insights that a bug-fixing commit is not only within reach but also the overall quality posture of the system is improving. In this thesis, we perform a quantitative analysis of how the posture of a software module changes and whether those changes follow a pattern that can be used as a predictor for an imminent bug-fixing commit. In this thesis, the posture of a module is denoted by a vector of metrics values computed from the source code and from information extracted from GitHub and Bugzilla repositories. The results indicate that a considerable number of bug-fixing commits in many software projects is preceded by a typical posture, and the occurrences of some posture combinations are more likely than others to be succeeded by a bug-fixing commit
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