Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamodel and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice.

Context: Artificial Intelligence (AI) is increasingly integrated into critical domains, making defect analysis essential to ensure system quality and reliability. Current defect classification frameworks do not adequately address the unique properties of AI systems. Objective: This paper proposes AIODC, a defect classification framework inspired by the Orthogonal Defect Classification (ODC) that incorporates AI-specific characteristics. Method: The framework extends ODC by introducing three new attributes – Data, Learning, and Thinking – and adds a “Catastrophic” severity level to account for risks associated with AI. Additionally, it modifies impact mapping utilizing AI/AIP quality models. The methodology was validated through a case study that examined 42 actual Keras defects. Results: This study demonstrated the feasibility of modifying ODC for AI systems to classify its defects. The case study indicated that defects occurring during the Learning phase are the most prevalent and were significantly linked to high severity, whereas defects in the Thinking phase primarily impact trustworthiness and accuracy. Conclusions: The results affirm the practicality and significance of AIODC in identifying high-risk defect categories, thus facilitating more focused and effective quality assurance strategies in AI-driven software systems.

Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamodel and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice.

Poor communication of requirements between clients and suppliers contributes to project overruns,in both software and infrastructure projects. Existing literature offers limited insights into the communication challenges at this interface. Our research aim to explore the processes and associated challenges with requirements activities that include client-supplier interaction and communication. we study requirements validation, communication, and digital asset verification processes through two case studies in the road and railway sectors, involving interviews with ten experts across three companies. We identify 13 challenges, along with their causes and consequences, and suggest solution areas from existing literature. Interestingly, the challenges in infrastructure projects mirror those found in software engineering, highlighting a need for further research to validate potential solutions.

Background: With the growing advances in the digital world, software development demands are increasing at an exponential rate. To ensure reliability of the software, high-performance tools for bug report analysis are needed. Aim: This paper proposes a new ‘Iterative Software Reliability’ model based on one of the most recent Software Development Life Cycle (SDLC) approach. Method: The proposed iterative failure rate model assumes that new functionality enhancement occurs in each iteration of software development and accordingly design modification is made at each stage of software development. In terms of defects, testing effort, and added functionality, these changing needs in each iteration are reflected in the proposed model using iterative factors. The proposed model has been tested on twelve Eclipse and six JDT software failure datasets. Proposed model parameters have been estimated using a hybrid swarm - evolutionary algorithm. Results: The proposed model has about 32% and 55% improved efficiency on Eclipse and JDT datasets respectively as compared to other models like Jelinski Moranda Model, Shick-Wolverton Model, Goel Okumotto Imperfect Model etc. Conclusion: In each analysis done, the proposed model is found to be reaching acceptable performance and could be applied on other software failure datasets for further validation.

Context: Action research is popular in software engineering due to its industrial nature and promises of effective technology transfers. Yet, the methodology is still gaining popularity, and guidelines for conducting quality action research studies are needed. Objective: This paper aims to collect, summarize, and discuss guidelines for conducting action research in academia-industry collaborations. The guidelines are designed for researchers and practitioners alike. Method: I use existing guidelines for empirical studies and my own experiences to define guidelines for researchers and host organizations for conducting action research. Results: I identified 22 guidelines for conducting action research studies. They provide actionable recommendations on identifying the relevant context, planning and executing interventions (actions), reporting them, and reasoning around the ethics of action research. Conclusions: The paper concludes that the best way of engaging with action research is when we can be embedded in the host organization and when the collaboration leads to tangible change in the host organization and the generation of new scientific results.

Background: With the rapid proliferation of question-and-answer websites for software developers like Stack Overflow, there is an increasing need to discern developers’ emotions from their posts to assess the influence of these emotions on their productivity such as efficiency in bug fixing. Aim: We aimed to develop a reliable emotion classification tool capable of accurately categorizing emotions in Software Engineering (SE) websites using data augmentation techniques to address the data scarcity problem because previous research has shown that tools trained on other domains can perform poorly when applied to SE domain directly. Method: We utilized four machine learning techniques, namely BERT, CodeBERT, RFC (Random Forest Classifier), and LSTM. Taking an innovative approach to dataset augmentation, we employed word substitution, back translation, and easy data augmentation methods. Using these we developed sixteen unique emotion classification models: textit EmoClassBERT-Original, textit EmoClassRFC-Original, textit EmoClassLSTM-Original, textit EmoClassCodeBERT-Original textit EmoClassLSTM-Substitution, textit EmoClassBERT-Substitution, textit EmoClassRFC-Substitution, textit EmoClassCodeBERT-Substitution, textit EmoClassBERT-Translation, textit EmoClassLSTM-Translation, textit EmoClassRFC-Translation, textit EmoClassCodeBERT-Translation, textit EmoClassBERT-EDA, textit EmoClassLSTM-EDA, textit EmoClassCodeBERT-EDA, and textit EmoClassRFC-EDA. We compared the performance of this model on a gold standard state-of-the-art database and techniques (Multi-label SO BERT and EmoTxt). Results: An initial investigation of models trained on the augmented datasets demonstrated superior performance to those trained on the original dataset. EmoClassLSTM-Substitution, EmoClassBERT-Substitution, EmoClassCodeBERT-Substitution, and EmoClassRFC-Substitution models show improvements of 13%, 5%, 5%, and 10% as compared to EmoClassLSTM-Original, EmoClassBERT-Original, EmoClassCodeBERT-Original, and EmoClassRFC-Original, respectively, in average F1 score. The textit EmoClassCodeBERT-Substitution performed the best and outperformed the Multi-label SO BERT and Emotxt by 2.37% and 21.17%, respectively, in average F1-score. A detailed investigation of the models on 100 runs of the dataset shows that BERT-based and CodeBERT-based models gave the best performance. This detailed investigation reveals no significant differences in the performance of models trained on augmented datasets and the original dataset on multiple runs of the dataset. Conclusion: This research not only underlines the strengths and weaknesses of each architecture but also highlights the pivotal role of data augmentation in refining model performance, especially in the software engineering domain.

Background: Modern Code Reviews (MCR) are frequently adopted when assuring code and design quality in continuous integration and deployment projects. Although tiresome, they serve a secondary purpose of learning about the software product. Aim: Our objective is to design and evaluate a support tool to help software developers focus on the most important code fragments to review and provide them with suggestions on what should be reviewed in this code. Method: We used design science research to develop and evaluate a tool for automating code reviews by providing recommendations for code reviewers. The tool is based on Transformer-based machine learning models for natural language processing, applied to both programming language code (patch content) and the review comments. We evaluate both the ability of the language model to match similar lines and the ability to correctly indicate the nature of the potential problems encoded in a set of categories. We evaluated the tool on two open-source projects and one industry project. Results: The proposed tool was able to correctly annotate (only true positives) 35%–41% and partially correctly annotate 76%–84% of code fragments to be reviewed with labels corresponding to different aspects of code the reviewer should focus on. Conclusion: By comparing our study to similar solutions, we conclude that indicating lines to be reviewed and suggesting the nature of the potential problems in the code allows us to achieve higher accuracy than suggesting entire changes in the code considered in other studies. Also, we have found that the differences depend more on the consistency of commenting rather than on the ability of the model to find similar lines.

Background: Early identification of software vulnerabilities is an intrinsic step in achieving software security. In the era of artificial intelligence, software vulnerability prediction models (VPMs) are created using machine learning and deep learning approaches. The effectiveness of these models aids in increasing the quality of the software. The handling of imbalanced datasets and dimensionality reduction are important aspects that affect the performance of VPMs. Aim: The current study applies novel metaheuristic approaches for feature subset selection. Method: This paper performs a comparative analysis of forty-eight combinations of eight machine learning techniques and six metaheuristic feature selection methods on four public datasets. Results: The experimental results reveal that VPMs productivity is upgraded after the application of the feature selection methods for both metrics-based and text-mining-based datasets. Additionally, the study has applied Wilcoxon signed-rank test to the results of metrics-based and text-features-based VPMs to evaluate which outperformed the other. Furthermore, it discovers the best-performing feature selection algorithm based on AUC for each dataset. Finally, this paper has performed better than the benchmark studies in terms of F1-Score. Conclusion: The results conclude that GWO has performed satisfactorily for all the datasets.

Background: Continuous modifications, suboptimal software design practices, and stringent project deadlines contribute to the proliferation of code smells. Detecting and refactoring these code smells are pivotal to maintaining complex and essential software systems. Neglecting them may lead to future software defects, rendering systems challenging to maintain, and eventually obsolete. Supervised machine learning techniques have emerged as valuable tools for classifying code smells without needing expert knowledge or fixed threshold values. Further enhancement of classifier performance can be achieved through effective feature selection techniques and the optimization of hyperparameter values. Aim: Performance measures of multiple machine learning classifiers are improved by fine tuning its hyperparameters using various type of meta-heuristic algorithms including swarm intelligent, physics, math, and bio-based etc. Their performance measures are compared to find the best meta-heuristic algorithm in the context of code smell detection and its impact is evaluated based on statistical tests. Method: This study employs sixteen contemporary and robust meta-heuristic algorithms to optimize the hyperparameters of two machine learning algorithms: Support Vector Machine (SVM) and k-nearest Neighbors (k-NN). The No Free Lunch theorem underscores that the success of an optimization algorithm in one application may not necessarily extend to others. Consequently, a rigorous comparative analysis of these algorithms is undertaken to identify the best-fit solutions for code smell detection. A diverse range of optimization algorithms, encompassing Arithmetic, Jellyfish Search, Flow Direction, Student Psychology Based, Pathfinder, Sine Cosine, Jaya, Crow Search, Dragonfly, Krill Herd, Multi-Verse, Symbiotic Organisms Search, Flower Pollination, Teaching Learning Based, Gravitational Search, and Biogeography-Based Optimization, have been implemented. Results: In the case of optimized SVM, the highest attained accuracy, AUC, and F-measure values are 98.75%, 100%, and 98.57%, respectively. Remarkably, significant increases in accuracy and AUC, reaching 32.22% and 45.11% respectively, are observed. For k-NN, the best accuracy, AUC, and F-measure values are all perfect at 100%, with noteworthy hikes in accuracy and ROC-AUC values, amounting to 43.89% and 40.83%, respectively. Conclusion: Optimized SVM exhibits exceptional performance with the Sine Cosine Optimization algorithm, while k-NN attains its peak performance with the Flower Optimization algorithm. Statistical analysis underscores the substantial impact of employing meta-heuristic algorithms for optimizing machine learning classifiers, enhancing their performance significantly. Optimized SVM excels in detecting the God Class, while optimized k-NN is particularly effective in identifying the Data Class. This innovative fusion automates the tuning process and elevates classifier performance, simultaneously addressing multiple longstanding challenges.