Journal of Software-Evolution and Process最新文献

{"title":"Method for the Development of Biometric Systems Based on Scrum and ISO/IEC 24745 Standard","authors":"John Escobar-Ucrus,&nbsp;David Mauricio,&nbsp;Gloria Arcos-Medina","doi":"10.1002/smr.70141","DOIUrl":"https://doi.org/10.1002/smr.70141","url":null,"abstract":"<div>\u0000 \u0000 <p>Biometric systems play a crucial role in institutions where security is a priority. However, there is currently no specific guideline addressing security deficiencies during their development. This study proposes an agile method for the development of biometric systems, designed to meet the strict security and privacy requirements of the ISO/IEC 24745 standard. The method combines the five phases of the Scrum methodology (initiation, planning, implementation, retrospective, and release), 19 processes from the Scrum Body of Knowledge Guide (SBOK), 120 best practices (ABP), and 19 specific activities aimed at meeting the requirements of the standard. To evaluate its effectiveness, two biometric systems were developed: one for prison record management, using the proposed method, and another for prison visit management, using traditional Scrum. Both projects were carried out by the same team of engineers. The results showed that the proposed method increased agility by 40% and improved security by 71% compared with the traditional methodology. These results highlight the method's ability to optimize both agility and security in the development of biometric systems, emphasizing its relevance in contexts where data protection and efficiency are essential.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281970","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":"Factors to Foster Resilience Against Coding Stressors of Agile Software Developers","authors":"Ramón R. Palacio,&nbsp;Gilberto M. Córdova,&nbsp;Alba C. Martínez,&nbsp;Gilberto Borrego,&nbsp;Aurora Vizcaíno","doi":"10.1002/smr.70139","DOIUrl":"https://doi.org/10.1002/smr.70139","url":null,"abstract":"<p>IT workers, especially software developers, undergo high stress levels due to their intellectual activity, which requires creativity and problem-solving skills for specific situations. These circumstances often lead to a fast-paced lifestyle, including sleep deprivation, which is often worn as a badge of honor. These demanding routines can have long-term negative consequences for their health. Therefore, software developers need to understand the dimensions that positively affect resilience with respect to job demand-control. In this work, the problem of how software developers address the pressure caused by the workload through resilience was addressed; some important findings of this study are the following: (1) The greater the social support, meaning the work done by software developers is supported and recognized, the higher the level of resilience the subjects of the sample present. (2) There is a higher level of job control and social support in male programmers compared with female programmers, as well as individuals who suffer from high blood pressure have less resilience and job control. (3) The levels of resilience are proportional to the perception that the employee has about job control; it is understood that the greater job control the developer feels about his responsibilities, the greater the resilience will be.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237932","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":"Syn2Vec: A Semantic-Aware Initialization Method for AST Path–Based Code Representation","authors":"Xuekai Sun,&nbsp;Shi Dong,&nbsp;Xianyu Zuo","doi":"10.1002/smr.70145","DOIUrl":"https://doi.org/10.1002/smr.70145","url":null,"abstract":"<div>\u0000 \u0000 <p>Although abstract syntax tree (AST) path–based approaches have proven effective, the impact of node embedding initialization methods on model performance remains understudied. This work presents a comprehensive investigation of node embedding initialization for AST path–based code analysis models, proposing Syn2Vec, a semantic-aware initialization method. Our approach learns the semantic relationships between AST nodes in path contexts through a pre-training process, simultaneously capturing structural features and contextual dependencies. This generates initial embeddings that integrate both syntactic and semantic information. Syn2Vec effectively models the structural relatedness and semantic correlation between AST nodes, providing superior initialization for downstream code analysis models. Experimental results on standard code classification benchmarks demonstrate that Syn2Vec-initialized models achieve a 9.43% improvement in the F1-score compared with random initialization, conclusively establishing the importance of initialization strategies for the analysis of AST path–based code.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237485","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":"Studying the Evolution of TensorFlow Questions on Stack Overflow","authors":"Gbolahan Michael Adebesin,&nbsp;Sangeeta Sangeeta","doi":"10.1002/smr.70140","DOIUrl":"https://doi.org/10.1002/smr.70140","url":null,"abstract":"<div>\u0000 \u0000 <p>Deep learning has revolutionized various fields, including computer vision, natural language processing, and robotics. Python, with its simplicity and extensive libraries, has emerged as one of the primary programming languages for implementing deep learning models, and several frameworks like TensorFlow, Gaffe, and PyTorch are proposed by machine learning communities/companies for Python. TensorFlow is one of the most popular frameworks in Python for deep learning; hence, it is important to investigate the kind of issue that TensorFlow developers face. In this work, we analyzed 105,437 TensorFlow questions from the Stack Overflow website and answered five research questions. Our analysis reveals that, despite a decreasing trend in the number of questions asked, there is a high level of user engagement and satisfaction in the TensorFlow community. Our tag analysis reveals that “python,” “Keras,” “deep-learning,” “machine-learning,” and “neural-network” are the top tags associated with TensorFlow questions. We perform topic analysis and reveal four main topics: <i>machine learning and data processing</i>, <i>neural network and architecture</i>, <i>TensorFlow installation and usage</i>, and <i>TensorFlow manipulation and operation</i>. Further analysis reveals that <i>error</i>, <i>model training and prediction</i>, <i>optimization</i>, <i>data loading and preprocessing</i>, and <i>installation and deployment</i> are among the most frequently occurring subtopics. <i>ValueError</i> is the most common error and the <i>model training and prediction</i> subtopic is the most affected by errors. Our analysis reveals that the <i>TensorFlow installation and usage</i> is the most popular and difficult topic. Our study also reveals a high percentage (i.e., 31%) of broken links in TensorFlow questions.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237439","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":"Empirical Analysis of Challenges in Implementing Agile Methodologies in Global Software Development","authors":"Aiman Fatima,&nbsp;Muhammad Yaseen,&nbsp;Gohar Rahman,&nbsp;Umar Farooq Khattak,&nbsp;Sikandar Ali,&nbsp;Ahmed Alkhayyat,&nbsp;Fazal Hameed,&nbsp;Waqar Mehmood","doi":"10.1002/smr.70128","DOIUrl":"https://doi.org/10.1002/smr.70128","url":null,"abstract":"<div>\u0000 \u0000 <p>Agile processes have become a key factor in the continuously changing sector of contemporary software development, fundamentally affecting conventional project management approaches. In modern society, the global software development system has become a worldwide business, spreading daily. While integrating software engineering with agile methodologies offers benefits, it also presents several challenges. First, all possible challenges are identified from the literature via a systematic literature review (SLR). Next, these challenges are reviewed by software industries using a questionnaire survey across various types and levels of experts and then by comparing SLR and Questionnaire Survey. As a result of SLR, 18 challenges were identified from a total of 60 articles that were finalized after executing SLR protocol. The identified challenges were analyzed based on expert role, experience, and type. Further analysis of identified challenges was based on SLR result comparison with questionnaire survey results. This study tries to fill the gap between empirical results and literature findings. Results of systematic literature and empirical study will help industrial experts to have a better understanding of the effect of challenges of agile in GSD.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237562","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 Deep Learning Approach to Automated Bug Triaging: Investigating the Impact of Text Vectorization Methods on Effectiveness of CNN-LSTM","authors":"Ava Faghihi,&nbsp;Seyed Mostafa Fakhrahmad,&nbsp;Mohammad Hadi Sadreddini","doi":"10.1002/smr.70146","DOIUrl":"https://doi.org/10.1002/smr.70146","url":null,"abstract":"<div>\u0000 \u0000 <p>Manual bug triage is a significant bottleneck in modern software development, leading to costly project delays. While automated systems offer a solution, their performance is fundamentally tied to their ability to comprehend the semantic content of bug reports. The text representation methods that power these systems have evolved significantly, yet a systematic comparison to understand their true impact on triage accuracy has been lacking. This paper conducts a large-scale empirical study to fill this gap, comparing nine text vectorization methods—spanning from classical TF-IDF to state-of-the-art Sentence-Transformers (SBERT)—within a hybrid CNN-LSTM framework. We evaluate the models on six benchmark datasets from projects like Google Chromium and Mozilla. Performance is measured using Top-k accuracy, a standard metric for this recommendation-style task, which assesses whether the correct developer is ranked among the top predictions. Our findings reveal that sentence-level embeddings from SBERT consistently and significantly achieve the highest Top-k accuracy, outperforming all other techniques, including contextual models like BERT. Our SBERT-based model establishes a new state-of-the-art, demonstrating that a holistic, sentence-level semantic understanding is critical for effective bug-to-developer assignment.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174409","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":"Advancing Software Reliability: A Nonhomogeneous Poisson Process Model Integrating Dependent Failures, Testing Coverage, and Operational Uncertainties","authors":"Farhan Mateen Khan,&nbsp;Asim Munir,&nbsp;Shujaat Ali,&nbsp;Tahir Jameel,&nbsp;Mohammed Asmat Ullah Khan,&nbsp;Dilawar Shah,&nbsp;Muhammad Tahir","doi":"10.1002/smr.70120","DOIUrl":"https://doi.org/10.1002/smr.70120","url":null,"abstract":"<div>\u0000 \u0000 <p>Software reliability growth models are essential for assessing software quality, predicting failure behavior, and supporting release planning during the software development lifecycle. However, many existing models rely on simplifying assumptions, such as independent failures, perfect debugging, and fixed operational conditions, which may limit their predictive accuracy in real-world software systems. To address these limitations, this study proposes a generalized Software Reliability Growth Model based on the Non-Homogeneous Poisson Process framework. The proposed model integrates dependent failure behavior, testing coverage, fault detection intensity, repair intensity, and uncertainty in operational usage profiles. By incorporating fault dependency dynamics into the mean value function, the model provides a more realistic representation of how software failures occur and evolve during testing. The model parameters are estimated using the Maximum Likelihood Estimation method, and its performance is evaluated using 2 real-world software failure datasets. Comparative analysis is conducted against several established independent and dependent failure models using multiple goodness-of-fit and predictive accuracy criteria, including Mean Squared Error, Mean Absolute Error, Adjusted R-squared, Akaike Information Criterion, Root Mean Square Prediction Error, Predictive Power, Predictive Ratio Risk, and Theil Statistic. The results show that the proposed model achieves superior or highly competitive performance across both datasets, particularly in terms of error reduction and model fit. In addition, an optimal software release-time framework is developed to examine the effect of testing cost, error removal cost, installation cost, failure penalty cost, and expected usage duration on release decisions. The findings indicate that the proposed model can support more accurate reliability assessment, cost-sensitive release planning, and improved software quality management. Overall, the study provides a practical and analytically flexible framework for modeling software reliability under dependent failures, testing coverage variation, and operational uncertainty.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173956","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":"Unearthing Code Smells: An In-Depth Exploration of Machine Learning Techniques in Code Smell Detection","authors":"Ishita Kheria,&nbsp;Dhruv Gada,&nbsp;Vijay Harkare,&nbsp;Ruhina Karani","doi":"10.1002/smr.70144","DOIUrl":"https://doi.org/10.1002/smr.70144","url":null,"abstract":"<div>\u0000 \u0000 <p>Code smells are clear indicators of design flaws that compromise maintainability, reliability, and overall performance in software systems. As systems become complex, traditional detection methods become inadequate, prompting the need for automated approaches. In this study, we evaluate the potential of conventional machine learning algorithms for detecting critical code smells. Specifically, we examine Classification and Regression Trees (CART), Gradient Boosting Classifiers, Extreme Gradient Boosting (XGBoost), RuleFit, Adaptive Boosting (AdaBoost), <i>K</i>-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), and Random Forest to identify six key code smell categories: Large Class, Long Parameter List, Switch Statements, God Class, Data Class, and Long Method. Advanced resampling techniques such as SMOTE–ENN and SMOTE–Tomek are employed to mitigate dataset imbalances alongside tree-based feature extraction for effective dimensionality reduction. Systematic experimentation demonstrates that SMOTE-based strategies significantly enhance detection performance, yielding robust results across multiple evaluation metrics. By focusing on the core challenges of code smell detection, this study provides a cost-effective alternative to deep learning methods, which often demand extensive computational resources and large labeled datasets.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173990","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":"On Predicting Vulnerability Severity Using In-Context Learning: An Industrial Case Study","authors":"Daniel Rodriguez-Cardenas,&nbsp;David N. Palacio,&nbsp;Anna Schmedding,&nbsp;Yiyang Lu,&nbsp;Aadil Mallick,&nbsp;Bill Hudson,&nbsp;Chris Gourley,&nbsp;Michael Roytman,&nbsp;Chris Shenefiel,&nbsp;Evgenia Smirni,&nbsp;Denys Poshyvanyk","doi":"10.1002/smr.70126","DOIUrl":"https://doi.org/10.1002/smr.70126","url":null,"abstract":"<p>Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Security analysts typically assign CVSS scores, but this manual triage does not scale with the growth of disclosed vulnerabilities and often depends on cloud LLM services that raise confidentiality concerns. This paper presents an industrial case study on predicting CVSS v3.1 scores directly from vulnerable C/C++ snippets using <i>in-context learning</i> with locally deployable, <i>open-source</i> LLMs. We compare <i>Propietary</i> data with the <i>Big-Vul</i> dataset, showing sufficiently aligned CVSS distributions to justify <i>Big-Vul</i> as a proxy for industrial data when constructing prompt-based testbeds. We then vary in-context configurations and model parameters, evaluating <i>CodeLlama2-7B</i>, <i>CodeLlama2-13B</i>, <i>Mistral-7B</i>, <i>gpt-oss</i>, and <i>GPT4o-mini</i> using mean squared error (MSE) and feasibility metrics. Our results show that medium-sized open-source code models, particularly <i>CodeLlama2-7B</i>, can approximate the best cloud performance for CVSS regression when guided by lightweight, output-constraining prompts, offering a practical, privacy-preserving building block for severity triage in industrial settings.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.70126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148129424","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":"Exploring Success Factors and Practices for Mob Programming: Extraction From MLR and Empirical Study","authors":"Rafi Ullah,&nbsp;Muhammad Ilyas","doi":"10.1002/smr.70130","DOIUrl":"https://doi.org/10.1002/smr.70130","url":null,"abstract":"<div>\u0000 \u0000 <p>Mob Programming (MP) is an emerging, relatively new, and unexplored programming technique that is becoming increasingly popular and receiving attention in the software industry. It is a collaborative programming approach where the entire development team works together on a single task. This study explores the success factors (SFs) and their implementation practices for the effective use of MP in the software industry. The research was conducted in two phases: First, a multivocal literature review (MLR) analyzed formal and gray literature (GL), reviewing 76 primary studies to identify SFs and their implementation practices for adopting MP. In the second phase, an empirical survey was conducted, involving 106 software industry experts from 30 countries, to validate the MLR findings. The study identified 12 SFs, among which four factors, “professional communication and team collaboration,” “skills and wills,” “proper feedback sessions,” and “self-organized, self-motivated, and cooperative teams” were ranked as critical success factors (CSFs). Similarly, 126 implementation practices were discovered for the implementation of the identified SFs. The findings suggest that empirical methods are commonly used in MP studies and interest in MP has grown significantly over the past decade. The survey confirmed that the identified SFs are prevalent across projects and organizations of varying sizes. These results offer valuable insights to enhance developers' competence and promote the successful implementation of the MP in the software industry.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"38 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148128167","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}