Journal of Software-Evolution and Process最新文献

{"title":"Multi-objective optimization-based and fault localization-oriented test case generation for novice programs","authors":"Yong Liu,&nbsp;Zezhong Yang,&nbsp;Luxi Fan,&nbsp;Yonghao Wu,&nbsp;Xiang Chen,&nbsp;Xiaotang Zhou","doi":"10.1002/smr.2679","DOIUrl":"10.1002/smr.2679","url":null,"abstract":"<div>\u0000 \u0000 <p>Online judgment (OJ) systems are capable of evaluating program results by automatically executing test cases, significantly improving the efficiency of traditional guidance approaches. Moreover, existing studies attempt to assist novices through automated fault localization techniques to provide feedback to novices, which can help them quickly find the location of faulty statements. Among them, spectrum-based fault localization (SBFL) techniques have been widely used for their lightweight and efficiency, which only requires coverage information and test results of test cases to conduct fault localization. However, manually constructing high-quality test cases for a large number of OJ questions is tough work to complete. To solve this problem, we propose the novice program-oriented <b>M</b>ulti-Objective Optimization-Based <b>F</b>ault Localization-Oriented <b>T</b>est <b>C</b>ase <b>G</b>eneration (MFTCG) for automatically generating test inputs. Specifically, we use multi-objective optimization algorithms to evolve the test case in terms of both fault localization and faulty code detection capability. We conduct experiments with 8911 programs from the well-known public OJ platform AtCoder. The results show that our proposed approach MFTCG can achieve the best fault localization performance compared with existing automated test case generation approaches in most cases and can achieve the similar faulty code detection capability compared to manually designed test cases.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 10","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197635","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":"Special issues on emerging technologies and their importance for software and systems processes","authors":"Xiwei Xu,&nbsp;Jens Heidrich","doi":"10.1002/smr.2678","DOIUrl":"10.1002/smr.2678","url":null,"abstract":"&lt;p&gt;The integration of these emerging technologies into software and systems processes is not merely a matter of technological advancement but a fundamental shift in how software is conceived, developed, and deployed. As organizations strive to innovate and stay competitive in today's digital economy, harnessing the power of AI, big data, blockchain, AR, and other emerging technologies is imperative for driving efficiency, agility, and innovation in software and systems development. By embracing these technologies and adapting to the evolving technological landscape, software developers and organizations can unlock new opportunities, address complex challenges, and deliver transformative solutions that meet the evolving needs of users and stakeholders.&lt;/p&gt;&lt;p&gt;The 17th International Conference on Software and System Processes (ICSSP) held in 2023 focused on the theme “Software and System Processes for and with Emerging Technologies.” This theme reflects the accelerating pace of technological advancement and its profound impact on software and system development practices. The conference provided a platform for researchers and practitioners to explore innovative approaches in incorporating new technologies into software and system processes, aiming to advance the state of both research and practice in the field.&lt;/p&gt;&lt;p&gt;This special issue of the Journal of Software: Evolution and Process features a selection of high-quality articles from ICSSP 2023 that exemplify the conference theme. The articles selected for inclusion cover a range of topics related to software and system processes, highlighting the diverse applications of emerging technologies in improving development practices and how to improve our engineering processes when developing systems using emerging technologies.&lt;/p&gt;&lt;p&gt;The selection process for the seven articles featured in this special issue involved a rigorous evaluation of the scientific contributions presented at the ICSSP 2023. Out of the 10 scientific articles presented at the conference, the editorial team carefully selected the four most outstanding articles based on their originality, relevance to the conference theme, and contribution to advancing software and systems processes. These selected articles underwent an extended review process and were expanded into comprehensive articles for inclusion in this special issue. Additionally, the three keynote speakers at the conference were invited to contribute articles summarizing their research presentations and providing valuable insights into the field. It is important to note that all submitted articles, including those selected for this special issue, underwent thorough peer reviews by experts in the field before being accepted for publication in the journal. The inclusion of these keynote articles further enriches this special issue by offering diverse perspectives and cutting-edge research in software and systems processes.&lt;/p&gt;&lt;p&gt;The articles featured in this special issue can","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115613","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":"Enhancing agile project success: a comprehensive study of risk management approaches among Malaysian practitioners","authors":"Mazni Omar,&nbsp;Abdul Rehman Gilal,&nbsp;Mazida Ahmad,&nbsp;Huda Ibrahim,&nbsp;Azman Yasin,&nbsp;Hapini Awang,&nbsp;Abdullah Almogahed","doi":"10.1002/smr.2681","DOIUrl":"10.1002/smr.2681","url":null,"abstract":"<p>Risk management (RM) plays a role in project management in the software development field. As information technology (IT) systems become more essential across industries and IT projects continue to face failure rates effective project management becomes crucial. However, the utilization of methodologies in risk management is not widely considered, specifically in Malaysia. This study aims to investigate how software practitioners in Malaysia have implemented risk management and discover strategies that can enhance risk management for agile contexts. The main focus of this study is the limited integration of methodologies into risk management practices, which has created a gap within the software risk domain. Successful risk management is essential for the achievement of software projects, and the findings from this study can offer insights for software development organizations to make informed decisions and improve project outcomes. By utilizing a quantitative approach and adapted questionnaires, this comprehensive study collected data from 60 practitioners and conducted descriptive analysis to identify key risk elements that have significant potential to affect project performance. The findings highlight these risk elements that can significantly impact project success. Agile methodologies, with their emphasis on collaboration, communication within teams, and engagement with stakeholders, including top management, are instrumental in aligning project objectives, identifying potential risks, and resolving issues promptly. This study provides empirical insights into the risk management practices of agile practitioners in Malaysia, which can equip software development organizations with valuable knowledge for informed decision-making. By enhancing project outcomes and guiding future strategic actions, the findings of this study can contribute to the improvement of agile risk management in the software development industry, particularly in the Malaysian context.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123879","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":"Sustainable software engineering—A contribution puzzle of different teams in large IT organizations","authors":"Alexander Poth,&nbsp;Pegah Momen","doi":"10.1002/smr.2677","DOIUrl":"10.1002/smr.2677","url":null,"abstract":"<div>\u0000 \u0000 <p>IT sustainability is becoming more and more important within the digitalization journey. It is more than green coding or the usage of power-efficient hardware – it needs a holistic approach to leverage the potentials end-to-end in the software life-cycle. The overall value stream within the IT organization is subject to sustainability alignment. This holistic alignment demands the engagement of different teams in large IT organizations. This article identifies the contributions of typical team stakeholders and evaluates their sustainability software engineering (SSE) contributions. This includes demand, architecture, design, implementation, operation, and usage. As an IT organization focuses on more than just software, the term sustainable IT engineering (SIE) fits better with the scope of organizational sustainability. The proposed approaches are presented based on instantiations within the Volkswagen Group IT.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985506","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 novel defect prediction method based on semantic feature enhancement","authors":"Chi Zhang,&nbsp;Xiaoli Wang,&nbsp;Jinfu Chen,&nbsp;Saihua Cai,&nbsp;Rexford Nii Ayitey Sosu","doi":"10.1002/smr.2674","DOIUrl":"10.1002/smr.2674","url":null,"abstract":"<div>\u0000 \u0000 <p>Although cross-project defect prediction (CPDP) techniques that use traditional manual features to build defect prediction model have been well-developed, they usually ignore the semantic and structural information inside the program and fail to capture the hidden features that are critical for program category prediction, resulting in poor defect prediction results. Researchers have proposed using deep learning to automatically extract the semantic features of programs and fuse them with traditional features as training data. However, in practice, it is important to explore the effective representation of the semantic features in the programs and how the fusion of a reasonable ratio between the two types of features can maximize the effectiveness of the model. In this paper, we propose a semantic feature enhancement-based defect prediction framework (SFE-DP), which augments the semantic feature set extracted from the program code with data. We also introduce a layer of self-attentive mechanism and a matching layer to filter low-efficiency and non-critical semantic features in the model structure. Finally, we combine the idea of hybrid loss function to iteratively optimize the model parameters. Extensive experiments validate that SFE-DP can outperform the baseline approaches on 90 pairs of CPDP tasks formed by 10 open-source projects.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140842212","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":"WELL: Applying bug detectors to bug localization via weakly supervised learning","authors":"Huangzhao Zhang,&nbsp;Zhuo Li,&nbsp;Jia Li,&nbsp;Zhi Jin,&nbsp;Ge Li","doi":"10.1002/smr.2669","DOIUrl":"10.1002/smr.2669","url":null,"abstract":"<p>Bug localization, which is used to help programmers identify the location of bugs in source code, is an essential task in software development. Researchers have already made efforts to harness the powerful deep learning (DL) techniques to automate it. However, training bug localization model is usually challenging because it requires a large quantity of data labeled with the bug's exact location, which is difficult and time-consuming to collect. By contrast, obtaining bug detection data with binary labels of whether there is a bug in the source code is much simpler. This paper proposes a WEakly supervised bug LocaLization (WELL) method, which only uses the bug detection data with binary labels to train a bug localization model. With CodeBERT finetuned on the buggy-or-not binary labeled data, WELL can address bug localization in a weakly supervised manner. The evaluations on three method-level synthetic datasets and one file-level real-world dataset show that WELL is significantly better than the existing state-of-the-art model in typical bug localization tasks such as variable misuse and other bugs.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800248","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":"PCG: A joint framework of graph collaborative filtering for bug triaging","authors":"Jie Dai,&nbsp;Qingshan Li,&nbsp;Shenglong Xie,&nbsp;Daizhen Li,&nbsp;Hua Chu","doi":"10.1002/smr.2673","DOIUrl":"10.1002/smr.2673","url":null,"abstract":"<p>Bug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering-based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629479","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":"Software development effort estimation using boosting algorithms and automatic tuning of hyperparameters with Optuna","authors":"Maryam Hassanali,&nbsp;Mohammadreza Soltanaghaei,&nbsp;Taghi Javdani Gandomani,&nbsp;Farsad Zamani Boroujeni","doi":"10.1002/smr.2665","DOIUrl":"10.1002/smr.2665","url":null,"abstract":"<p>Considering the increasing need for software projects, estimating software development efforts is essential and can lead to improved project delivery quality. Machine learning methods are widely used to improve the accuracy of estimation. The boosting method is an ensemble machine learning technique less used in this field. In this research, five boosting algorithms including Adaboost, Gradient boosting, XGBoost, LightGBM, and CatBoost were implemented with the hyperparameter tuning framework Optuna on the ISBSG database. The Optuna is a next-generation optimization method for automatically tuning hyperparameters of algorithms. Six evaluation criteria MMRE, MdMRE, MAE, MSE, Pred(0.25), and SA were used to evaluate the findings. The results show that the hyperparameter automatic tuning by Optuna increases the accuracy of prediction provided by all five models. When the Catboost algorithm uses Optuna to tune its hyperparameters has made the best prediction among the five algorithms studied in this research. Using Optuna, compared to the case where the algorithm uses its default settings, the highest percentage of prediction improvement was observed in the XGBoost algorithm (except for the SA criterion). Based on the criteria of MMRE, Pred(0.25), and SA, this study has a better prediction than some relatively similar articles.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576994","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":"Gas-centered mutation testing of Ethereum Smart Contracts","authors":"Pedro Delgado-Pérez,&nbsp;Ignacio Meléndez-Lapi,&nbsp;Juan Boubeta-Puig","doi":"10.1002/smr.2672","DOIUrl":"10.1002/smr.2672","url":null,"abstract":"<p>Smart contracts (SC) are programs embodying certain business logic stored on a blockchain network like Ethereum. The execution of transactions on SC has a cost, measured in gas units, that depends on the low-level operations performed. Therefore, a poor choice of high-level language constructs could lead to overcharging users for their transactions. Thus, a testing process focused on possible deviations of the gas used in diverse scenarios could provide substantial global savings. This paper presents a gas-centered mutation testing approach for taking care of the gas consumed by Solidity SCs. This approach can be useful to improve the test quality to detect gas-related problems, reason about performance issues that only manifest in certain situations, and identify alternative more optimal implementations. We define and implement several mutation operators specifically designed to perturb gas consumption while preserving contract semantics in general. Our experiments using several real-world SCs show the feasibility of the technique, with some mutants reproducing meaningful differences in the consumption and exposing some gas limits not tight enough in historic transactions. Therefore, our approach is shown to be a good ally to prevent the appearance of gas-related issues and lays the groundwork for researchers seeking to improve performance testing practices.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577454","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":"Context factors perceived important when looking for similar experiences in decision-making for software components: An interview study","authors":"Efi Papatheocharous,&nbsp;Claes Wohlin,&nbsp;Deepika Badampudi,&nbsp;Jan Carlson,&nbsp;Krzysztof Wnuk","doi":"10.1002/smr.2668","DOIUrl":"10.1002/smr.2668","url":null,"abstract":"<p>During software evolution, decisions related to components' origin or source significantly impact the quality properties of the product and development metrics such as cost, time to market, ease of maintenance, and further evolution. Thus, such decisions should ideally be supported by evidence, i.e., using previous experiences and information from different sources, even own previous experiences. A hindering factor to such reuse of previous experiences is that these decisions are highly context-dependent and it is difficult to identify when previous experiences come from sufficiently similar contexts to be useful in a current setting. Conversely, when documenting a decision (as a decision experience), it is difficult to know which context factors will be most beneficial when reusing the experience in the future. An interview study is performed to identify a list of context factors that are perceived to be most important by practitioners when using experiences to support decision-making for component sourcing, using a specific scenario with alternative sources of experiences. We observed that the further away (from a company or an interviewee) the experience evidence is, as is the case for online experiences, the more context factors are perceived as important by practitioners to make use of the experience. Furthermore, we discuss and identify further research to make this type of decision-making more evidence-based.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"36 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576954","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}