Information and Software Technology最新文献

{"title":"Feature envy detection based on cross-graph local semantics matching","authors":"Quanxin Yang,&nbsp;Dongjin Yu,&nbsp;Xin Chen,&nbsp;Yihang Xu,&nbsp;Wangliang Yan,&nbsp;Bin Hu","doi":"10.1016/j.infsof.2024.107515","DOIUrl":"10.1016/j.infsof.2024.107515","url":null,"abstract":"<div><h3>Context:</h3><p>As a typical code smell, feature envy occurs when a method exhibits excessive reliance and usage on specific functionalities of another class, which can lead to issues with the maintainability and extensibility of the code. As such, detecting and avoiding feature envy is critical for software development. Previous research on detecting feature envy has demonstrated significant advantages of deep learning-based approaches over static code analysis tools. However, current deep learning-based approaches still suffer from two limitations: (1) They focus on the functional or overall semantics of the code, which ignores the opportunities for local code semantics matching, making it challenging to identify some more complex cases; (2) Existing feature envy datasets are collected or synthesized using static code analysis tools, which limits feature envy cases to fixed rules and makes it challenging to cover other complex cases in real projects.</p></div><div><h3>Objective:</h3><p>We are motivated to propose a Siamese graph neural network based on code local semantics matching and collect feature envy refactoring cases from real projects for experimental evaluation.</p></div><div><h3>Method:</h3><p>To address the first issue, we propose a cross-graph local semantics matching network, which aims to simulate human intuition or experience to detect feature envy by analyzing the local semantics matching between code graphs. To address the second one, we manually review and collect commits for refactoring feature envy cases on GitHub. Then, we refer to image data augmentation technology to construct two datasets for identifying feature envy and recommending <em>Move Method</em> refactorings, respectively.</p></div><div><h3>Results:</h3><p>Extensive experiments show that our approach outperforms state-of-the-art baselines regarding both tasks’ comprehensive metrics, F1-score and AUC.</p></div><div><h3>Conclusion:</h3><p>The experimental results indicate that the proposed Siamese graph neural network based on code local semantics matching is effective. In addition, the provided data augmentation algorithms can significantly improve model performance.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107515"},"PeriodicalIF":3.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141407138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"SENEM: A software engineering-enabled educational metaverse","authors":"Viviana Pentangelo,&nbsp;Dario Di Dario,&nbsp;Stefano Lambiase,&nbsp;Filomena Ferrucci,&nbsp;Carmine Gravino,&nbsp;Fabio Palomba","doi":"10.1016/j.infsof.2024.107512","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107512","url":null,"abstract":"<div><h3>Context:</h3><p>The term metaverse refers to a persistent, virtual, three-dimensional environment where individuals may communicate, engage, and collaborate. One of the most multifaceted and challenging use cases of the metaverse is education, where educators and learners may require multiple technical, social, psychological, and interaction instruments to accomplish their learning objectives. While the characteristics of the metaverse might nicely fit the problem’s needs, our research points out a noticeable lack of knowledge into (1) the specific requirements that an educational metaverse should actually fulfill to let educators and learners successfully interact towards their objectives and (2) how to design an appropriate educational metaverse for both educators and learners.</p></div><div><h3>Objective:</h3><p>In this paper, we aim to bridge this knowledge gap by proposing <span>SENEM</span>, a novel software engineering-enabled educational metaverse. We first elicit a set of functional requirements that an educational metaverse should fulfill.</p></div><div><h3>Method:</h3><p>In this respect, we conduct a literature survey to extract the currently available knowledge on the matter discussed by the research community, and afterward, we assess and complement such knowledge through semi-structured interviews with educators and learners. Upon completing the requirements elicitation stage, we then build our prototype implementation of <span>SENEM</span>, a metaverse that makes available to educators and learners the features identified in the previous stage. Finally, we evaluate the tool in terms of learnability, efficiency, and satisfaction through a Rapid Iterative Testing and Evaluation research approach, leading us to the iterative refinement of our prototype.</p></div><div><h3>Results:</h3><p>Through our survey strategy, we extracted nine requirements that guided the tool development that the study participants positively evaluated.</p></div><div><h3>Conclusion:</h3><p>Our study reveals that the target audience appreciates the elicited design strategy. Our work has the potential to form a solid contribution that other researchers can use as a basis for further improvements.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107512"},"PeriodicalIF":3.9,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001174/pdfft?md5=d195f9e835855108574c749267de7cd3&pid=1-s2.0-S0950584924001174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"On current limitations of online eye-tracking to study the visual processing of source code","authors":"Eva Thilderkvist,&nbsp;Felix Dobslaw","doi":"10.1016/j.infsof.2024.107502","DOIUrl":"10.1016/j.infsof.2024.107502","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context:&lt;/h3&gt;&lt;p&gt;Eye-tracking is an increasingly popular instrument to study how programmers process and comprehend source code. While most studies are conducted in controlled environments with lab-grade hardware, it would be desirable to simplify and scale participation in experiments for users sitting remotely, leveraging home equipment.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective:&lt;/h3&gt;&lt;p&gt;This study investigates the possibility of performing eye-tracking studies remotely using open-source algorithms and consumer-grade webcams. It establishes the technology’s current limitations and evaluates the quality of the data collected by it. We conclude by recommending ways forward to address the shortcomings and make remote code-reading studies in support of eye-tracking feasible in the future.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Method:&lt;/h3&gt;&lt;p&gt;We gathered eye-gaze data remotely from 40 participants performing a code reading experiment on a purpose-built web application. The utilized eye-tracker worked client-side and used ridge regression to generate x- and y-coordinates in real-time predicting the participants’ on-screen gaze points without the need to collect and save video footage. We processed and analysed the collected data according to common practices for isolating eye-movement events and deriving metrics used in software engineering eye-tracking studies. In response to the lack of an algorithm explicitly developed for detecting oculomotor fixation events in low-frequency webcam data, we also introduced a dispersion threshold algorithm for that purpose. The quality of the collected data was subsequently assessed to determine the adequacy and validity of the methodology for eye-tracking.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;p&gt;The collected data was found to be of varying quality despite extensive calibration and graphical user guidance. We present our results highlighting both the negative and positive observations from which the community hopefully can learn. Both accuracy and precision were low and ultimately deemed insufficient for drawing valid conclusions in a high-precision empirical study. We nonetheless contribute to identifying critical limitations to be addressed in future research. Apart from the overall challenge of vastly diverse equipment, setup, and configuration, we found two main problems with the current webcam eye-tracking technology. The first was the absence of a validated algorithm to isolate fixations in low-frequency data, compromising the assurance of the accuracy of the data derived from it. The second problem was the lack of algorithmic support for head movements when predicting gaze location. Unsupervised participants do not always keep their heads still, even if instructed to do so. Consequently, we frequently observed spatial shifts that corrupted many collected datasets. Three encouraging observations resulted from the study. Even when shifted, gaze points were consistently dispersed in patterns resembling both the shape and size of the stimuli w","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107502"},"PeriodicalIF":3.8,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001071/pdfft?md5=4163070dc71f52245e9d0dc96b0daa9b&pid=1-s2.0-S0950584924001071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141397559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Reporting case studies in systematic literature studies—An evidential problem","authors":"Austen Rainer ,&nbsp;Claes Wohlin","doi":"10.1016/j.infsof.2024.107501","DOIUrl":"10.1016/j.infsof.2024.107501","url":null,"abstract":"<div><h3>Context:</h3><p>The term and label, “case study”, is not used consistently by authors of primary studies in software engineering research. It is not clear whether this problem also occurs for systematic literature studies (SLSs).</p></div><div><h3>Objective:</h3><p>To investigate the extent to which SLSs in/correctly use the term and label, “case study”, when classifying primary studies.</p></div><div><h3>Methods:</h3><p>We systematically collect two sub-samples (2010–2021 &amp; 2022) comprising a total of eleven SLSs and 79 primary studies. We examine the designs of these SLSs, and then analyse whether the SLS authors and the primary-study authors correctly label the respective primary study as a “case study”.</p></div><div><h3>Results:</h3><p>76% of the 79 primary studies are misclassified by SLSs (with the two sub-samples having 60% and 81% misclassification, respectively). For 39% of the 79 studies, the SLSs propagate a mislabelling by the original authors, whilst for 37%, the SLSs introduce a new mislabel, thus making the problem worse. SLSs rarely present explicit definitions for “case study” and when they do, the definition is not consistent with established definitions.</p></div><div><h3>Conclusions:</h3><p>SLSs are both propagating and exacerbating the problem of the mislabelling of primary studies as “case studies”, rather than – as we should expect of SLSs – correcting the labelling of primary studies, and thus improving the body of credible evidence. Propagating and exacerbating mislabelling undermines the credibility of evidence in terms of its quantity, quality and relevance to both practice and research.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107501"},"PeriodicalIF":3.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S095058492400106X/pdfft?md5=3d35e744355dfcb7dc6216c05d335658&pid=1-s2.0-S095058492400106X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141274816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Analysing the synergies between Multi-agent Systems and Digital Twins: A systematic literature review","authors":"Elena Pretel,&nbsp;Alejandro Moya,&nbsp;Elena Navarro,&nbsp;Víctor López-Jaquero,&nbsp;Pascual González","doi":"10.1016/j.infsof.2024.107503","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107503","url":null,"abstract":"<div><h3>Context</h3><p>Digital Twins (DTs) are used to augment physical entities by exploiting assorted computational approaches applied to the virtual twin counterpart. A DT is generally described as a physical entity, its virtual counterpart, and the data connections between them. Multi-Agent Systems (MAS) paradigm is alike DTs in many ways. Agents of MAS are entities operating and interacting in a specific environment, while exploring and collecting data to solve some tasks.</p></div><div><h3>Objective</h3><p>This paper presents the results of a systematic literature review (SLR) focused on the analysis of current proposals exploiting the synergies of DTs and MAS. This research aims to synthesize studies that focus on the use of MAS to support DTs development and MAS that exploit DTs, paving the way for future research.</p></div><div><h3>Method</h3><p>A SLR methodology was used to conduct a detailed study analysis of 64 primary studies out of a total of 220 studies that were initially identified. This SLR analyses three research questions related to the synergies between MAS and DT.</p></div><div><h3>Results</h3><p>The most relevant findings of this SLR and their implications for further research are the following: i) most of the analyzed proposals design digital shadows rather than DT; ii) they do not fully support the properties expected from a DT; iii) most of the MAS properties have not fully exploited for the development of DT; iv) ontologies are frequently used for specifying semantic models of the physical twin.</p></div><div><h3>Conclusions</h3><p>Based on the results of this SLR, our conclusions for the community are presented in a research agenda that highlights the need of innovative theoretical proposals and design frameworks that guide the development of DT. They should be defined exploiting the properties of MAS to unleash the full potential of DT. Finally, ontologies for machine learning models should be designed for its use in DT.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107503"},"PeriodicalIF":3.9,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001083/pdfft?md5=48481e9833bc2f65ff6732893530e9b3&pid=1-s2.0-S0950584924001083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141294267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Multi-objective model transformation chain exploration with MOMoT","authors":"Martin Eisenberg ,&nbsp;Apurvanand Sahay ,&nbsp;Davide Di Ruscio ,&nbsp;Ludovico Iovino ,&nbsp;Manuel Wimmer ,&nbsp;Alfonso Pierantonio","doi":"10.1016/j.infsof.2024.107500","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107500","url":null,"abstract":"<div><h3>Context:</h3><p>The increasing complexity of modern systems leads to an increasing amount of artifacts that are used along the model-based software and systems development lifecycle. This also includes model transformations, which serve for mapping models between representations, e.g., for verification and validation purposes.</p></div><div><h3>Objectives:</h3><p>Model repositories manage this variety of artifacts and promote reusability, but should also enable the bundling of compatible artifacts. Therefore, model transformations should be reused and arranged into transformation chains to support more complex transformation scenarios. The resulting transformation should correspond to the user’s interest in terms of quality criteria such as model coverage, transformation coverage, and number of transformation steps, thus assembling such chains becomes a multi-objective problem.</p></div><div><h3>Methods:</h3><p>A novel multi-objective approach for exploring possible transformation chains residing in model repositories is presented. MOMoT, a model-driven optimization framework, is leveraged to explore the transformation space spanned by the repository. For demonstration, three differently populated repositories are considered.</p></div><div><h3>Results:</h3><p>We have extended MOMoT with an exhaustive, multi-objective search that explores the entire model transformation space defined by graph transformation rules, allowing all possible transformation chains to be considered as solution. Accordingly, the optimal solutions were identified in the demonstration cases with negligible computation time.</p></div><div><h3>Conclusion:</h3><p>The approach assists modelers when there are multiple chains for transforming an input model to a specified output model to consider. Our evaluation shows that the approach elicits all legitimate transformation chains, thus enabling the modelers to consider trade-offs in view of multiple criteria selection.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107500"},"PeriodicalIF":3.9,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001058/pdfft?md5=799f0416b65e1e077e4134f38b7e8d28&pid=1-s2.0-S0950584924001058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141313437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Boosting fault localization of statements by combining topic modeling and Ochiai","authors":"Romain Vacheret ,&nbsp;Francisca Pérez ,&nbsp;Tewfik Ziadi ,&nbsp;Lom Hillah","doi":"10.1016/j.infsof.2024.107499","DOIUrl":"10.1016/j.infsof.2024.107499","url":null,"abstract":"<div><h3>Context:</h3><p>Reducing the cost of maintenance tasks by fixing bugs automatically is the cornerstone of Automated Program Repair (APR). To do this, automated Fault Localization (FL) is essential. Two families of FL techniques are Spectrum-based Fault Localization (SBFL) and Information Retrieval Fault Localization (IRFL). In SBFL, the coverage information and execution results of test cases are utilized. Ochiai is one of the most effective and used SBFL strategies. In IRFL, the bug report information is utilized as well as the identifier names and comments in source code files. Latent Dirichlet Allocation (LDA) is a generative statistical model and one of the most popular topic modeling methods. However, LDA has been used at the method level of granularity as IRFL technique, whereas most existing APR tools are focused on the statement level.</p></div><div><h3>Objective:</h3><p>This paper presents our approach that combines topic modeling and Ochiai to boost FL at the statement level.</p></div><div><h3>Method:</h3><p>We evaluate our approach considering five different projects in Defects4J benchmark. We report the performance of our approach in terms of hit@k and MRR. To study the impact on the results, we compare our approach against five baselines: two SBFL approaches (Ochiai and Dstar), two IRFL approaches (LDA and Blues), and one hybrid approach (SBIR). In addition, we compare the number of bugs that are found by our approach with the baselines.</p></div><div><h3>Results:</h3><p>Our approach significantly outperforms the baselines in all metrics. Especially, when hit@1, hit@3 and hit@5 are compared. Also, our approach locates more bugs than Ochiai and Blues.</p></div><div><h3>Conclusion:</h3><p>The results of our approach indicate that the integration of topic modeling with Ochiai boosts FL. This uncovers the potential of topic modeling for FL at statement level, which is valuable for the APR community.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"173 ","pages":"Article 107499"},"PeriodicalIF":3.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141139785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Understanding the landscape of software modelling assistants for MDSE tools: A systematic mapping","authors":"David Mosquera ,&nbsp;Marcela Ruiz ,&nbsp;Oscar Pastor ,&nbsp;Jürgen Spielberger","doi":"10.1016/j.infsof.2024.107492","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107492","url":null,"abstract":"<div><h3>Context</h3><p>Model Driven Software Engineering (MDSE) and low-code/no-code software development tools promise to increase quality and productivity by modelling instead of coding software. One of the major advantages of modelling software is the increased possibility of involving diverse stakeholders since it removes the barrier of being IT experts to actively participate in software production processes. From an academic and industry point of view, the main question remains: What has been proposed to assist humans in software modelling tasks?</p></div><div><h3>Objective</h3><p>In this paper, we systematically elucidate the state of the art in assistants for software modelling and their use in MDSE and low-code/no-code tools.</p></div><div><h3>Method</h3><p>We conducted a systematic mapping to review the state of the art and answer the following research questions: i) how is software modelling assisted? ii) what goals and limitations do existing modelling assistance proposals report? iii) which evaluation metrics and target users do existing modelling assistance proposals consider? For this purpose, we selected 58 proposals from 3.176 screened records and reviewed 17 MDSE and low-code/no-code tools from main market players published by the Gartner Magic Quadrant.</p></div><div><h3>Result</h3><p>We clustered existing proposals regarding their modelling assistance strategies, goals, limitations, evaluation metrics, and target users, both in research and practice.</p></div><div><h3>Conclusions</h3><p>We found that both academic and industry proposals recognise the value of assisting software modelling. However, documentation about MDSE assistants’ limitations, evaluation metrics, and target users is scarce or non-existent. With the advent of artificial intelligence, we expect more assistants for MDSE and low-code/no-code software development will emerge, making imperative the need for well-founded frameworks for designing modelling assistants focused on addressing target users’ needs and advancing the state of the art.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"173 ","pages":"Article 107492"},"PeriodicalIF":3.9,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924000971/pdfft?md5=6da25ebb0cb2c28f672df388b54839e1&pid=1-s2.0-S0950584924000971-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141090894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Automating modern code review processes with code similarity measurement","authors":"Yusuf Kartal ,&nbsp;E. Kaan Akdeniz ,&nbsp;Kemal Özkan","doi":"10.1016/j.infsof.2024.107490","DOIUrl":"10.1016/j.infsof.2024.107490","url":null,"abstract":"<div><h3>Context:</h3><p>Modern code review is a critical component in software development processes, as it ensures security, detects errors early and improves code quality. However, manual reviews can be time-consuming and unreliable. Automated code review can address these issues. Although deep-learning methods have been used to recommend code review comments, they are expensive to train and employ. Instead, information retrieval (IR)-based methods for automatic code review are showing promising results in efficiency, effectiveness, and flexibility.</p></div><div><h3>Objective:</h3><p>Our main objective is to determine the optimal combination of the vectorization method and similarity to measure what gives the best results in an automatic code review, thereby improving the performance of IR-based methods.</p></div><div><h3>Method:</h3><p>Specifically, we investigate different vectorization methods (Word2Vec, Doc2Vec, Code2Vec, and Transformer) that differ from previous research (TF-IDF and Bag-of-Words), and similarity measures (Cosine, Euclidean, and Manhattan) to capture the semantic similarities between code texts. We evaluate the performance of these methods using standard metrics, such as Blue, Meteor, and Rouge-L, and include the run-time of the models in our results.</p></div><div><h3>Results:</h3><p>Our results demonstrate that the Transformer model outperforms the state-of-the-art method in all standard metrics and similarity measurements, achieving a 19.1% improvement in providing exact matches and a 6.2% improvement in recommending reviews closer to human reviews.</p></div><div><h3>Conclusion:</h3><p>Our findings suggest that the Transformer model is a highly effective and efficient approach for recommending code review comments that closely resemble those written by humans, providing valuable insight for developing more efficient and effective automated code review systems.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"173 ","pages":"Article 107490"},"PeriodicalIF":3.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"The impact of personality and self-efficacy on domain modeling productivity in graphical and textual notations","authors":"Santiago Meliá,&nbsp;Raymari Reyes,&nbsp;Cristina Cachero","doi":"10.1016/j.infsof.2024.107491","DOIUrl":"10.1016/j.infsof.2024.107491","url":null,"abstract":"<div><h3>Context</h3><p>Software development is a complex and human-intensive activity, where human factors can have a significant impact on productivity and quality of results. To address the complexity of software, domain modeling has gained much importance, mainly due to software methodologies such as Model-Driven Engineering and Domain-Driven Design. In particular, domain modeling is an essential task that allows developers to understand and effectively represent the problem domain. However, domain modeling productivity can be affected by several human factors, including developers' personality and self-efficacy.</p></div><div><h3>Objective</h3><p>The study aims to explore the influence of human factors, specifically developers' personality and self-efficacy, on domain modeling productivity in graphical and textual notations.</p></div><div><h3>Method</h3><p>An empirical controlled study was conducted with 134 third-year computer science students from the University of Alicante, guided by the definition of a theoretical model based on previous studies. The participants were tasked with creating domain models in both graphical and textual notations. The order in which the notations were used was randomized, and the participants were given different system specifications to model. After modeling, 98 participants completed questionnaires assessing their personality, self-efficacy, and notation satisfaction. The design and evaluation of the experiment employed the Goal, Question, and Metrics framework. Data analysis was performed using a stepwise selection method to select the most appropriate regression model.</p></div><div><h3>Results</h3><p>The study indicates that personality and self-efficacy have a significant impact on the performance of junior domain model developers. Specifically, it was discovered that while neuroticism had a negative impact on efficiency in both notations, developers' ability belief and use of graphical notation had a positive influence on effectiveness and efficiency in creating domain models.</p></div><div><h3>Conclusions</h3><p>These findings highlight the importance of considering human factors and notation choice in software development. Developers' personality and self-efficacy emerge as critical considerations for enhancing both productivity and quality in domain modeling.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"173 ","pages":"Article 107491"},"PeriodicalIF":3.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S095058492400096X/pdfft?md5=fde59a750411fe7c3eafe32ed36a6b20&pid=1-s2.0-S095058492400096X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}