Journal of Software-Evolution and Process最新文献

{"title":"Explainable AI Framework for Software Defect Prediction","authors":"Bahar Gezici Geçer,&nbsp;Ayça Kolukısa Tarhan","doi":"10.1002/smr.70018","DOIUrl":"https://doi.org/10.1002/smr.70018","url":null,"abstract":"<div>\u0000 \u0000 <p>Software engineering plays a critical role in improving the quality of software systems, because identifying and correcting defects is one of the most expensive tasks in software development life cycle. For instance, determining whether a software product still has defects before distributing it is crucial. The customer's confidence in the software product will decline if the defects are discovered after it has been deployed. Machine learning-based techniques for predicting software defects have lately started to yield encouraging results. The software defect prediction system's prediction results are raised by machine learning models. More accurate models tend to be more complicated, which makes them harder to interpret. As the rationale behind machine learning models' decisions are obscure, it is challenging to employ them in actual production. In this study, we employ five different machine learning models which are random forest (RF), gradient boosting (GB), naive Bayes (NB), multilayer perceptron (MLP), and neural network (NN) to predict software defects and also provide an explainable artificial intelligence (XAI) framework to both locally and globally increase openness throughout the machine learning pipeline. While global explanations identify general trends and feature importance, local explanations provide insights into individual instances, and their combination allows for a holistic understanding of the model. This is accomplished through the utilization of Explainable AI algorithms, which aim to reduce the “black-boxiness” of ML models by explaining the reasoning behind a prediction. The explanations provide quantifiable information about the characteristics that affect defect prediction. These justifications are produced using six XAI methods, namely, SHAP, anchor, ELI5, LIME, partial dependence plot (PDP), and ProtoDash. We use the KC2 dataset to apply these methods to the software defect prediction (SDP) system, and provide and discuss the results.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilabel Vulnerability Classification in Decentralized Blockchain–Based Reputation System","authors":"Balaji Barmavat,&nbsp;Dhanaraju M,&nbsp;K. Sreerama Murthy,&nbsp;Hari Krishna Madthala,&nbsp;Satya Krupa Prakash Karey,&nbsp;Rajesh Palthya","doi":"10.1002/smr.70024","DOIUrl":"https://doi.org/10.1002/smr.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>Smart contracts serve as decentralized applications essential for extensive utilization of blockchain technology across various contexts that have transitioned from the blockchain, characterized primarily by digital currency systems that emphasize the financial systems. Blockchain operates as a distributed ledger that securely records transactions using cryptographic techniques to establish a unique, chain-like data structure managed collectively by miners within the network. However, current methods for analyzing smart contracts often demand substantial processing time and face challenges in accurately detecting vulnerabilities in complex contracts. To address these limitations, this research introduces the Updated Wave search Graph Bidirectional Convolutional Neural Network (UWGBCNN), a novel approach designed to enhance smart contract security. UWGBCNN integrates a multilabel vulnerability classification mechanism, utilizing the Updated Wave Search Algorithm (UWSA) to efficiently analyze and identify patterns in smart contracts by adapting network parameters to detect vulnerabilities with speed and precision. Additionally, feature extraction is enhanced through the Bidirectional Encoder Representations from Transformer (BERT) language model, incorporating supplementary word embedding features. The proposed technique achieves superior performance, reaching a precision of 98.5%, recall of 98.6%, and an F1-score of 99.6%, surpassing current methods. This approach contributes significantly to blockchain security by minimizing financial risks associated with vulnerabilities in decentralized applications.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CARLDA: An Approach for Stack Overflow API Mention Recognition Driven by Context and LLM-Based Data Augmentation","authors":"Zhang Zhang,&nbsp;Xinjun Mao,&nbsp;Shangwen Wang,&nbsp;Kang Yang,&nbsp;Tanghaoran Zhang,&nbsp;Yao Lu","doi":"10.1002/smr.70015","DOIUrl":"https://doi.org/10.1002/smr.70015","url":null,"abstract":"<div>\u0000 \u0000 <p>The recognition of Application Programming Interface (API) mentions in software-related texts is vital for extracting API-related knowledge, providing deep insights into API usage and enhancing productivity efficiency. Previous research identifies two primary technical challenges in this task: (1) differentiating APIs from common words and (2) identifying morphological variants of standard APIs. While deep learning-based methods have demonstrated advancements in addressing these challenges, they rely heavily on high-quality labeled data, leading to another significant data-related challenge: (3) the lack of such high-quality data due to the substantial effort required for labeling. To overcome these challenges, this paper proposes a context-aware API recognition method named CARLDA. This approach utilizes two key components, namely, Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM), to extract context at both the word and sequence levels, capturing syntactic and semantic information to address the first challenge. For the second challenge, it incorporates a character-level BiLSTM with an attention mechanism to grasp global character-level context, enhancing the recognition of morphological features of APIs. To address the third challenge, we developed specialized data augmentation techniques using large language models (LLMs) to tackle both in-library and cross-library data shortages. These techniques generate a variety of labeled samples through targeted transformations (e.g., replacing tokens and restructuring sentences) and hybrid augmentation strategies (e.g., combining real-world and generated data while applying style rules to replicate authentic programming contexts). Given the uncertainty about the quality of LLM-generated samples, we also developed sample selection algorithms to filter out low-quality samples (i.e., incomplete or incorrectly labeled samples). Moreover, specific datasets have been constructed to evaluate CARLDA's ability to address the aforementioned challenges. Experimental results demonstrate that (1) CARLDA significantly enhances F1 by 11.0% and the Matthews correlation coefficient (MCC) by 10.0% compared to state-of-the-art methods, showing superior overall performance and effectively tackling the first two challenges, and (2) LLM-based data augmentation techniques successfully yield high-quality labeled data and effectively alleviate the third challenge.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process Debt: Definition, Risks, and Management","authors":"Antonio Martini,&nbsp;Viktoria Stray,&nbsp;Terese Besker,&nbsp;Nils Brede Moe,&nbsp;Jan Bosch","doi":"10.1002/smr.70017","DOIUrl":"https://doi.org/10.1002/smr.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>Process debt, like technical debt, can be a source of short-term benefits but often leads to harmful consequences in the long term for a software organization. Despite its impact, the phenomenon of process debt has not been thoroughly explored in current literature, leaving a gap in understanding how it affects and is managed within organizations. This paper addresses this gap by defining process debt, describing its occurrence, the risks of its mismanagement, and showing examples of mitigation strategies. Our study began with an exploratory phase involving semi-structured interviews with sixteen practitioners across four international organizations, allowing us to gather diverse insights into the occurrence and management of process debt. Then, to deepen our understanding and validate our findings, we conducted a cross-company focus group with ten additional practitioners and analyzed fifty-eight observations and thirty-five interviews from a longitudinal case study. The analysis of the research findings led to a definition of process debt and a novel framework. We also report on the causes, consequences, and occurrence patterns of process debt over time. We present mitigation strategies and discuss which ones need further attention for future research. Our results suggest that the debt metaphor may help companies understand how to manage and improve their processes and make process-related decisions that are beneficial both in the short and long term.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prioritization of Functional Requirements Using Directed Graph and K-Means Clustering","authors":"Muhammad Yaseen,&nbsp;Muhammad Asif Nauman,&nbsp;Roobaea Alroobaea,&nbsp;Hamed Alsufyani,&nbsp;Umar Farooq Khattak","doi":"10.1002/smr.70019","DOIUrl":"https://doi.org/10.1002/smr.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>Functional requirements (FRs) prioritization is process of ranking of software FRs from development perspective such that which requirement to be implemented first and which should not. FRs prioritization is necessary as these requirements are interrelated such that one requirement is necessary for the implementation of another requirement. Also, when two parallel developers work on interrelated dependent requirements, requirements must be prioritized. Prioritizing small size requirements is not a big issue due to a fewer number of comparisons but when developers implement large size requirements such as enterprise resource planning (ERP), it requires a huge number of comparisons. Numerous techniques are suggested for FRs prioritization such as AHP, which yield more accurate results, but these techniques are not scalable for large size software requirements. In this research paper, a new prioritization approach based on graph and k-means clustering is suggested that will capture all dependencies from a list of FRs using a directed graph and then prioritize it with a clustering technique with fewer comparisons. The proposed technique based on directed graph and clustering approach is validated on ODOO ERP, which shows that with n-1 pairwise comparisons, requirements can be prioritized.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Software Design Affects Energy Performance: A Systematic Literature Review","authors":"Déaglán Connolly Bree,&nbsp;Mel Ó Cinnéide","doi":"10.1002/smr.70014","DOIUrl":"https://doi.org/10.1002/smr.70014","url":null,"abstract":"<p>Interest in the energy consumption of software has grown with rising energy costs and greater environmental awareness. Many approaches to research in this area have been proposed, from the examination of hardware and compiler optimizations to platform specific software modifications. However, the impact of general software design on energy efficiency remains unclear. The goal of this research is to summarize the findings of studies that empirically examine the impact of design patterns, code smells, and refactorings (which we collectively describe as <i>design elements</i>) on energy consumption. Our secondary goal is to provide an overview of the impact of these aspects of software design on energy performance and discuss the current state of the art. We present a systematic literature review (SLR) of papers that examine the impact of the aforementioned design elements on energy consumption. We perform a search through four major databases, a manual search through publications of eight conferences and five journals from 2010 through 2023, in addition to snowballing. We extract relevant data from the literature and present an overview of each experiment's setup, the data reported, and results for each design element studied. Beginning with a set of 8684 papers, we select 24 that include studies of these design elements. Overall, they provide data on 22 design patterns, 17 code smells, and 31 refactorings. Many studies are preliminary in nature, and contradictory findings are frequent. We present three main findings: (i) a wide array of design patterns, code smells, and refactorings have been examined from an energy performance perspective; (ii) many of these studies are preliminary in nature and indicate the need for further research; (iii) there has been little growth recently in publications empirically examining these aspects of software design.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving IT/Business Alignment in DevOps: Business Capability for Adopting BizDevOps","authors":"Guillermo Fuentes-Quijada,&nbsp;Francisco Ruiz-González,&nbsp;Angélica Caro","doi":"10.1002/smr.70016","DOIUrl":"https://doi.org/10.1002/smr.70016","url":null,"abstract":"<div>\u0000 \u0000 <p>As organizations increasingly adopt DevOps practices, they often face limitations in IT/business alignment. BizDevOps emerges as an evolutionary and complementary approach that integrates business perspectives directly into the software development lifecycle, aiming to address these limitations and enhance overall organizational performance. This study investigates how BizDevOps builds upon and extends DevOps practices, focusing on developing a business capability to facilitate their integration and improve alignment without compromising agility. The study develops the BizDevOps Business Capability (BizDevOps-<span>BC</span>) through metaethnographic analysis and design principles application. To validate the applicability of this capability, both a proof of concept and an expert opinion survey were conducted. The PoC was implemented in a DevOps organization to evaluate whether this business capability improves IT-business alignment while maintaining software development agility. The expert survey gathered qualitative insights from industry professionals and academics, evaluating the relevance of BizDevOps-<span>BC</span> in various organizational contexts. The findings suggest that BizDevOps-<span>BC</span> has the potential to enhance alignment between IT and business, offering a structured approach that, if properly implemented, could help organizations evolve their DevOps practices, further improving overall IT/business alignment, without compromising their existing agility.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Levy Flight and Greylag Goose Optimization for Enhanced Cross-Project Defect Prediction in Software Evolution","authors":"Kripa Sekaran,&nbsp;Sherly Puspha Annabel Lawrence","doi":"10.1002/smr.70013","DOIUrl":"https://doi.org/10.1002/smr.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>The cross-project defect prediction (CPDP) in software applications is crucial to predict defects and ensure software quality. The performance of the traditional CPDP models is degraded due to the class imbalance issue between different projects and differences in the data distribution. To overcome these limitations, a novel approach is proposed named as Levy flight–enabled greylag goose optimized UniXcoder-based stacked defect predictor (LFGGO-USDP) for the prediction of cross-project defects in the software engineering. In this paper, 23 software projects are selected from diverse datasets such as PROMISE, ReLink, AEEEM, and NASA that are preprocessed for enhancing reliability and reducing class imbalance issues. The transformation model maps source and target projects that are present in the feature space for enhancing predictive performances. During feature selection, the LF mechanism is embedded with the GGO algorithm to localize the features in the source code for enhancing diversity and minimizing local optimum issues. The integration of UniXcoder-based stacked bidirectional long short-term memory (U-SBiLSTM) is implemented as a cross-project defect predictor. The UniXcoder model extracts semantic information for source code tokenization. Then, the output of UniXcoder is fed as input to SBiLSTM, and the SBiLSTM model is applied to determine the relationship between the source code. After that, the output of UniXcoder (which contains the semantic features) is integrated with the output of SBiLSTM (which contains the sequential and temporal dependencies). After concatenating these features, the particular information is selected by using an attention mechanism for categorizing defective and nondefective classes. The experimental investigations are performed to analyze the nondefective and defective cases in software projects and numerical validation is conducted by applying different evaluation models for analyzing the superiority. The proposed model achieved the highest defect prediction accuracy of 0.986 compared to other existing approaches that demonstrates the proposed model provided better prediction outcomes.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Perspective Review on Embedded Systems Quality: State of the Field, Challenges, and Research Directions","authors":"Müge Canpolat Şahin,&nbsp;Ayça Kolukisa Tarhan","doi":"10.1002/smr.70007","DOIUrl":"https://doi.org/10.1002/smr.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>The use of embedded systems has increased significantly over the last decade with the proliferation of Internet of Things technology, automotive and healthcare innovations and the use of smart home appliances and consumer electronics. With this increase, the need for higher quality embedded systems has increased. There are various guidelines and standards, such as ISO/IEC 9126 and ISO/IEC 25010, for product quality evaluation. However, these guidelines cannot be directly applied to embedded systems due to the nature of these systems. Applying traditional quality standards or guidelines on these systems without modification may degrade the performance of the system, increase memory usage or energy consumption, or affect other critical physical metrics adversely. Consequently, several models and approaches have either been introduced or have adopted existing guidelines to produce high-quality embedded systems. With this motivation, to understand the state of the art, and to identify the research directions in the field, we conducted a systematic literature review (SLR). In our research, we have investigated studies published from 1980 to 2024 and provided a comprehensive review of the scientific literature on quality models, quality attributes, employed practices, and the challenges, gaps, and pitfalls in the field.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilabeled Emotions Classification in Software Engineering Text Using Convolutional Neural Networks and Word Embeddings","authors":"Atif Ali Wagan,&nbsp;Shuaiyong Li","doi":"10.1002/smr.70010","DOIUrl":"https://doi.org/10.1002/smr.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>Effective collaboration among software developers relies heavily on their ability to communicate efficiently, with emotions playing a pivotal role in this process. Emotions are widely used in human decision-making, making automated tools for emotion classification within developer communication channels essential. These tools can enhance productivity and collaboration by increasing awareness of fellow developers' emotions. Previous approaches, such as HOMER, RAKEL, and EmoTxt, have been proposed to classify emotions in Stack Overflow and Jira datasets at a finer granularity. However, these tools face performance challenges. To address these limitations, we aim to enhance multilabeled emotion classification performance by leveraging TextCNN, word embeddings, and hyper-parameter optimization. We validate the performance of this method by comparing it with the best previous methods for emotion classification in software engineering text. This approach achieves an F1-Micro score of 84.6001% on the Jira dataset and 76.9366% on the Stack Overflow dataset, showing an improvement of 3.5001% and 8.6366%, respectively. This advancement underscores the potential of this method in improving emotion classification performance, thereby fostering better collaboration and productivity among software developers.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}