Pub Date : 2024-06-24DOI: 10.1016/j.infsof.2024.107518
Mengxi Zhang , Huaxiao Liu , Shenning Song , Chunyang Chen , Pei Huang , Jian Zhao
Context:
Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend.
Objective:
In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components.
Method:
ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues.
Results:
Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models.
Conclusion:
To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently.
{"title":"Are your apps accessible? A GCN-based accessibility checker for low vision users","authors":"Mengxi Zhang , Huaxiao Liu , Shenning Song , Chunyang Chen , Pei Huang , Jian Zhao","doi":"10.1016/j.infsof.2024.107518","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107518","url":null,"abstract":"<div><h3>Context:</h3><p>Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend.</p></div><div><h3>Objective:</h3><p>In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components.</p></div><div><h3>Method:</h3><p>ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues.</p></div><div><h3>Results:</h3><p>Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models.</p></div><div><h3>Conclusion:</h3><p>To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107518"},"PeriodicalIF":3.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486232","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}
Pub Date : 2024-06-22DOI: 10.1016/j.infsof.2024.107519
Joseph S. Botros , Lamis F. Al-Qora'n , Amro Al-Said Ahmad
Context
Unlike resilience, antifragility describes systems that get stronger rather than weaker under stress and chaos. Antifragile systems have the capacity to overcome stressors and come out stronger, whereas resilient systems are focused on their capacity to return to their previous state following a failure. As technology environments become increasingly complex, there is a great need for developing software systems that can benefit from failures while continuously improving. Most applications nowadays operate in cloud environments. Thus, with this increasing adoption of Cloud-Native Systems they require antifragility due to their distributed nature.
Objective
The paper proposes UNFRAGILE framework, which facilitates the transformation of existing systems into antifragile systems. The framework employs chaos engineering to introduce failures incrementally and assess the system's response under such perturbation and improves the quality of system response by removing fragilities and introducing adaptive fault tolerance strategies.
Method
The UNFRAGILE framework's feasibility has been validated by applying it to a cloud-native using a real-world architecture to enhance its antifragility towards long outbound service latencies. The empirical investigation of fragility is undertaken, and the results show how chaos affects application performance metrics and causes disturbances in them. To deal with chaotic network latency, an adaptation phase is put into effect.
Results
The findings indicate that the steady stage's behaviour is like the antifragile stage's behaviour. This suggests that the system could self-stabilise during the chaos without the need to define a static configuration after determining from the context of the environment that the dependent system was experiencing difficulties.
Conclusion
Overall, this paper contributes to ongoing efforts to develop antifragile software capable of adapting to the rapidly changing complex environment. Overall, the research provides an operational framework for engineering software systems that learn and improve through exposure to failures rather than just surviving them.
{"title":"Towards antifragility of cloud systems: An adaptive chaos driven framework","authors":"Joseph S. Botros , Lamis F. Al-Qora'n , Amro Al-Said Ahmad","doi":"10.1016/j.infsof.2024.107519","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107519","url":null,"abstract":"<div><h3>Context</h3><p>Unlike resilience, antifragility describes systems that get stronger rather than weaker under stress and chaos. Antifragile systems have the capacity to overcome stressors and come out stronger, whereas resilient systems are focused on their capacity to return to their previous state following a failure. As technology environments become increasingly complex, there is a great need for developing software systems that can benefit from failures while continuously improving. Most applications nowadays operate in cloud environments. Thus, with this increasing adoption of Cloud-Native Systems they require antifragility due to their distributed nature.</p></div><div><h3>Objective</h3><p>The paper proposes UNFRAGILE framework, which facilitates the transformation of existing systems into antifragile systems. The framework employs chaos engineering to introduce failures incrementally and assess the system's response under such perturbation and improves the quality of system response by removing fragilities and introducing adaptive fault tolerance strategies.</p></div><div><h3>Method</h3><p>The UNFRAGILE framework's feasibility has been validated by applying it to a cloud-native using a real-world architecture to enhance its antifragility towards long outbound service latencies. The empirical investigation of fragility is undertaken, and the results show how chaos affects application performance metrics and causes disturbances in them. To deal with chaotic network latency, an adaptation phase is put into effect.</p></div><div><h3>Results</h3><p>The findings indicate that the steady stage's behaviour is like the antifragile stage's behaviour. This suggests that the system could self-stabilise during the chaos without the need to define a static configuration after determining from the context of the environment that the dependent system was experiencing difficulties.</p></div><div><h3>Conclusion</h3><p>Overall, this paper contributes to ongoing efforts to develop antifragile software capable of adapting to the rapidly changing complex environment. Overall, the research provides an operational framework for engineering software systems that learn and improve through exposure to failures rather than just surviving them.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107519"},"PeriodicalIF":3.8,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001241/pdfft?md5=4a3b226c5cce52766d27e46c6f346db1&pid=1-s2.0-S0950584924001241-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483209","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}
The quality of software systems is significantly influenced by design testability, an aspect often overlooked during the initial phases of software development. The implementation may deviate from its design, resulting in decreased testability at the integration and unit levels.
Objective
The objective of this study is to automatically identify low-testable parts in object-orientated design and enhance them by refactoring to design patterns. The impact of various design metrics mainly coupling (e.g., fan-in and fan-out) and inheritance (e.g., depth of inheritance tree and number of subclasses) metrics on design testability is measured to select the most appropriate refactoring candidates.
Method
The methodology involves creating a machine learning model for design testability prediction using a large dataset of Java classes, followed by developing an automated refactoring tool. The design classes are vectorized by ten design metrics and labeled with testability scores calculated from a mathematical model. The model computes testability based on code coverage and test suite size of classes that have already been tested via automatic tools. A voting regressor model is trained to predict the design testability of any class diagram based on these design metrics. The proposed refactoring tool for dependency injection and factory method is applied to various open-source Java projects, and its impact on design testability is assessed.
Results
The proposed design testability model demonstrates its effectiveness by satisfactorily predicting design testability, as indicated by a mean squared error of 0.04 and an R2 score of 0.53. The automated refactoring tool has been successfully evaluated on six open-source Java projects, revealing an enhancement in design testability by up to 19.11 %.
Conclusion
The proposed automated approach offers software developers the means to continuously evaluate and enhance design testability throughout the entire software development life cycle, mitigating the risk of testability issues stemming from design-to-implementation discrepancies.
{"title":"Measuring and improving software testability at the design level","authors":"Morteza Zakeri-Nasrabadi , Saeed Parsa , Sadegh Jafari","doi":"10.1016/j.infsof.2024.107511","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107511","url":null,"abstract":"<div><h3>Context</h3><p>The quality of software systems is significantly influenced by design testability, an aspect often overlooked during the initial phases of software development. The implementation may deviate from its design, resulting in decreased testability at the integration and unit levels.</p></div><div><h3>Objective</h3><p>The objective of this study is to automatically identify low-testable parts in object-orientated design and enhance them by refactoring to design patterns. The impact of various design metrics mainly coupling (<em>e.g.</em>, fan-in and fan-out) and inheritance (<em>e.g.</em>, depth of inheritance tree and number of subclasses) metrics on design testability is measured to select the most appropriate refactoring candidates.</p></div><div><h3>Method</h3><p>The methodology involves creating a machine learning model for design testability prediction using a large dataset of Java classes, followed by developing an automated refactoring tool. The design classes are vectorized by ten design metrics and labeled with testability scores calculated from a mathematical model. The model computes testability based on code coverage and test suite size of classes that have already been tested via automatic tools. A voting regressor model is trained to predict the design testability of any class diagram based on these design metrics. The proposed refactoring tool for dependency injection and factory method is applied to various open-source Java projects, and its impact on design testability is assessed.</p></div><div><h3>Results</h3><p>The proposed design testability model demonstrates its effectiveness by satisfactorily predicting design testability, as indicated by a mean squared error of 0.04 and an R<sup>2</sup> score of 0.53. The automated refactoring tool has been successfully evaluated on six open-source Java projects, revealing an enhancement in design testability by up to 19.11 %.</p></div><div><h3>Conclusion</h3><p>The proposed automated approach offers software developers the means to continuously evaluate and enhance design testability throughout the entire software development life cycle, mitigating the risk of testability issues stemming from design-to-implementation discrepancies.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107511"},"PeriodicalIF":3.8,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438939","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}
Pub Date : 2024-06-21DOI: 10.1016/j.infsof.2024.107510
Wangshu Liu , Ye Yue , Xiang Chen , Qing Gu , Pengzhan Zhao , Xuejun Liu , Jianjun Zhao
Context:
Constructing an effective defect prediction model relies on a substantial number of labeled program modules. Unfortunately, program module labeling is often time-consuming and error-prone. Semi-supervised software defect prediction (SSDP) can alleviate this issue by incorporating some labeled modules and the remaining unlabeled modules from the same project.
Objective:
However, previous SSDP methods ignore the significant influence of dependencies between software modules. The potential of knowledge distillation in leveraging labeled instances to guide the learning process and effectively utilizing information from unlabeled instances to improve SSDP performance has not been fully investigated.
Method:
We propose a novel approach SeDPGK. Specifically, to exploit the graph-structured knowledge, we first construct the program dependence graph to extract control and data dependencies among modules. Then we use graph neural networks (GNNs) to learn the graph representation of the module relationships and encode with the statement semantics of abstract syntax tree and traditional static features for diversity. Second, we integrate multiple GNNs jointly trained as teacher models to ensemble various styles of graph-based networks and generate trustworthy labels for unlabeled modules. Further, to preserve the teacher model’s sufficient structure and semantic knowledge, we adopt a trainable label propagation and multi-layer perception as the student model and mitigate the differences between the teacher and student models using two widespread knowledge distillation functions.
Results:
We conducted our experiments on 17 real-world projects. The experimental results show that SeDPGK outperforms semi-supervised baselines with an average improvement of 16.9% for PD, 42.5% for FAR, and 8.9% for AUC, respectively. Moreover, the performance improvement is consistently significant across multiple statistical tests.
Conclusion:
The effectiveness of SeDPGK comes from the aggregation of the different GNNs with heterogeneity. Moreover, the graph structure and semantic features hidden behind the source code play a crucial role in the distillation framework.
{"title":"SeDPGK: Semi-supervised software defect prediction with graph representation learning and knowledge distillation","authors":"Wangshu Liu , Ye Yue , Xiang Chen , Qing Gu , Pengzhan Zhao , Xuejun Liu , Jianjun Zhao","doi":"10.1016/j.infsof.2024.107510","DOIUrl":"https://doi.org/10.1016/j.infsof.2024.107510","url":null,"abstract":"<div><h3>Context:</h3><p>Constructing an effective defect prediction model relies on a substantial number of labeled program modules. Unfortunately, program module labeling is often time-consuming and error-prone. Semi-supervised software defect prediction (SSDP) can alleviate this issue by incorporating some labeled modules and the remaining unlabeled modules from the same project.</p></div><div><h3>Objective:</h3><p>However, previous SSDP methods ignore the significant influence of dependencies between software modules. The potential of knowledge distillation in leveraging labeled instances to guide the learning process and effectively utilizing information from unlabeled instances to improve SSDP performance has not been fully investigated.</p></div><div><h3>Method:</h3><p>We propose a novel approach SeDPGK. Specifically, to exploit the graph-structured knowledge, we first construct the program dependence graph to extract control and data dependencies among modules. Then we use graph neural networks (GNNs) to learn the graph representation of the module relationships and encode with the statement semantics of abstract syntax tree and traditional static features for diversity. Second, we integrate multiple GNNs jointly trained as teacher models to ensemble various styles of graph-based networks and generate trustworthy labels for unlabeled modules. Further, to preserve the teacher model’s sufficient structure and semantic knowledge, we adopt a trainable label propagation and multi-layer perception as the student model and mitigate the differences between the teacher and student models using two widespread knowledge distillation functions.</p></div><div><h3>Results:</h3><p>We conducted our experiments on 17 real-world projects. The experimental results show that SeDPGK outperforms semi-supervised baselines with an average improvement of 16.9% for PD, 42.5% for FAR, and 8.9% for AUC, respectively. Moreover, the performance improvement is consistently significant across multiple statistical tests.</p></div><div><h3>Conclusion:</h3><p>The effectiveness of SeDPGK comes from the aggregation of the different GNNs with heterogeneity. Moreover, the graph structure and semantic features hidden behind the source code play a crucial role in the distillation framework.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107510"},"PeriodicalIF":3.8,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483210","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}
Pub Date : 2024-06-15DOI: 10.1016/j.infsof.2024.107517
Jianxin Cheng , Yizhou Chen , Yongzhi Cao , Hanpin Wang
Context:
Vulnerability detection is critical to ensure software security, and detecting vulnerabilities in smart contract code is currently gaining massive attention. Existing deep learning-based vulnerability detection methods represent the code as a code structure graph and eliminate vulnerability-irrelevant nodes. Then, they learn vulnerability-related code features from the simplified graph for vulnerability detection. However, this simplified graph struggles to represent relatively complete structural information of code, which may affect the performance of existing vulnerability detection methods.
Objective:
In this paper, we present a novel Vulnerability Detection framework based on Critical Execution Paths (VDCEP), which aims to improve smart contract vulnerability detection.
Method:
Firstly, given a code structure graph, we deconstruct it into multiple execution paths that reflect rich structural information of code. To reduce irrelevant code information, a path selection strategy is employed to identify critical execution paths that may contain vulnerable code information. Secondly, a feature extraction module is adopted to learn feature representations of critical paths. Finally, we feed all path feature representations into a classifier for vulnerability detection. Also, the feature weights of paths are provided to measure their importance in vulnerability detection.
Results:
We evaluate VDCEP on a large dataset with four types of smart contract vulnerabilities. Results show that VDCEP outperforms 14 representative vulnerability detection methods by 5.34%–60.88% in F1-score. The ablation studies analyze the effects of our path selection strategy and feature extraction module on VDCEP. Moreover, VDCEP still outperforms ChatGPT by 34.46% in F1-score.
Conclusion:
Compared to existing vulnerability detection methods, VDCEP is more effective in detecting smart contract vulnerabilities by utilizing critical execution paths. Besides, we can provide interpretable details about vulnerability detection by analyzing the path feature weights.
{"title":"A vulnerability detection framework by focusing on critical execution paths","authors":"Jianxin Cheng , Yizhou Chen , Yongzhi Cao , Hanpin Wang","doi":"10.1016/j.infsof.2024.107517","DOIUrl":"10.1016/j.infsof.2024.107517","url":null,"abstract":"<div><h3>Context:</h3><p>Vulnerability detection is critical to ensure software security, and detecting vulnerabilities in smart contract code is currently gaining massive attention. Existing deep learning-based vulnerability detection methods represent the code as a code structure graph and eliminate vulnerability-irrelevant nodes. Then, they learn vulnerability-related code features from the simplified graph for vulnerability detection. However, this simplified graph struggles to represent relatively complete structural information of code, which may affect the performance of existing vulnerability detection methods.</p></div><div><h3>Objective:</h3><p>In this paper, we present a novel <strong>V</strong>ulnerability <strong>D</strong>etection framework based on <strong>C</strong>ritical <strong>E</strong>xecution <strong>P</strong>aths (VDCEP), which aims to improve smart contract vulnerability detection.</p></div><div><h3>Method:</h3><p>Firstly, given a code structure graph, we deconstruct it into multiple execution paths that reflect rich structural information of code. To reduce irrelevant code information, a path selection strategy is employed to identify critical execution paths that may contain vulnerable code information. Secondly, a feature extraction module is adopted to learn feature representations of critical paths. Finally, we feed all path feature representations into a classifier for vulnerability detection. Also, the feature weights of paths are provided to measure their importance in vulnerability detection.</p></div><div><h3>Results:</h3><p>We evaluate VDCEP on a large dataset with four types of smart contract vulnerabilities. Results show that VDCEP outperforms 14 representative vulnerability detection methods by 5.34%–60.88% in F1-score. The ablation studies analyze the effects of our path selection strategy and feature extraction module on VDCEP. Moreover, VDCEP still outperforms ChatGPT by 34.46% in F1-score.</p></div><div><h3>Conclusion:</h3><p>Compared to existing vulnerability detection methods, VDCEP is more effective in detecting smart contract vulnerabilities by utilizing critical execution paths. Besides, we can provide interpretable details about vulnerability detection by analyzing the path feature weights.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107517"},"PeriodicalIF":3.8,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141396583","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}
Pub Date : 2024-06-13DOI: 10.1016/j.infsof.2024.107516
Jorge Melegati , Igor Wiese , Eduardo Guerra , Rafael Chanin , Abdullah Aldaeej , Tommi Mikkonen , Rafael Prikladnicki , Xiaofeng Wang
Context:
Defining and designing a software product is not merely a technical endeavor, but also a socio-technical journey. As such, its success is associated with human-related aspects, such as the value users perceive. To handle this issue, the product manager role has become more evident in software-intensive companies. A unique, challenging context for these professionals is constituted by software startups, emerging companies developing novel solutions looking for sustainable and scalable business models.
Objective:
This study aims to describe the role of product managers in the context of software startups.
Method:
We performed a Socio-Technical Grounded Theory study using data from blog posts and interviews.
Results:
The results describe the product manager as a multidisciplinary, general role, not only guiding the product by developing its vision but also as a connector that emerges in a growing company, enabling communication of software development with other areas, mainly business and user experience. The professional performing this role has a background in one of these areas but a broad knowledge and understanding of key concepts of the other areas is needed. We also describe how differences of this role to other lead roles are perceived in practice.
Conclusions:
Our findings represent several implications for research, such as better understanding of the role transformation in growing software startups, practice, e.g., identifying the points to which a professional migrating to this role should pay attention, and the education of future software developers, by suggesting the inclusion of related topics in the education and training of future software engineers.
{"title":"Product managers in software startups: A grounded theory","authors":"Jorge Melegati , Igor Wiese , Eduardo Guerra , Rafael Chanin , Abdullah Aldaeej , Tommi Mikkonen , Rafael Prikladnicki , Xiaofeng Wang","doi":"10.1016/j.infsof.2024.107516","DOIUrl":"10.1016/j.infsof.2024.107516","url":null,"abstract":"<div><h3>Context:</h3><p>Defining and designing a software product is not merely a technical endeavor, but also a socio-technical journey. As such, its success is associated with human-related aspects, such as the value users perceive. To handle this issue, the product manager role has become more evident in software-intensive companies. A unique, challenging context for these professionals is constituted by software startups, emerging companies developing novel solutions looking for sustainable and scalable business models.</p></div><div><h3>Objective:</h3><p>This study aims to describe the role of product managers in the context of software startups.</p></div><div><h3>Method:</h3><p>We performed a Socio-Technical Grounded Theory study using data from blog posts and interviews.</p></div><div><h3>Results:</h3><p>The results describe the product manager as a multidisciplinary, general role, not only guiding the product by developing its vision but also as a connector that emerges in a growing company, enabling communication of software development with other areas, mainly business and user experience. The professional performing this role has a background in one of these areas but a broad knowledge and understanding of key concepts of the other areas is needed. We also describe how differences of this role to other lead roles are perceived in practice.</p></div><div><h3>Conclusions:</h3><p>Our findings represent several implications for research, such as better understanding of the role transformation in growing software startups, practice, e.g., identifying the points to which a professional migrating to this role should pay attention, and the education of future software developers, by suggesting the inclusion of related topics in the education and training of future software engineers.</p></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"174 ","pages":"Article 107516"},"PeriodicalIF":3.9,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0950584924001216/pdfft?md5=bdf7a653e0eee5cde2e2aff9308d4ff0&pid=1-s2.0-S0950584924001216-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141396895","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}
Pub Date : 2024-06-12DOI: 10.1016/j.infsof.2024.107515
Quanxin Yang, Dongjin Yu, Xin Chen, Yihang Xu, Wangliang Yan, Bin Hu
Context:
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.
Objective:
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.
Method:
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 Move Method refactorings, respectively.
Results:
Extensive experiments show that our approach outperforms state-of-the-art baselines regarding both tasks’ comprehensive metrics, F1-score and AUC.
Conclusion:
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.
{"title":"Feature envy detection based on cross-graph local semantics matching","authors":"Quanxin Yang, Dongjin Yu, Xin Chen, Yihang Xu, Wangliang Yan, 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}
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.
Objective:
In this paper, we aim to bridge this knowledge gap by proposing SENEM, a novel software engineering-enabled educational metaverse. We first elicit a set of functional requirements that an educational metaverse should fulfill.
Method:
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 SENEM, 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.
Results:
Through our survey strategy, we extracted nine requirements that guided the tool development that the study participants positively evaluated.
Conclusion:
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.
{"title":"SENEM: A software engineering-enabled educational metaverse","authors":"Viviana Pentangelo, Dario Di Dario, Stefano Lambiase, Filomena Ferrucci, Carmine Gravino, 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}
Pub Date : 2024-06-07DOI: 10.1016/j.infsof.2024.107502
Eva Thilderkvist, Felix Dobslaw
<div><h3>Context:</h3><p>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.</p></div><div><h3>Objective:</h3><p>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.</p></div><div><h3>Method:</h3><p>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.</p></div><div><h3>Results:</h3><p>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
背景：在研究程序员如何处理和理解源代码的过程中，眼动跟踪是一种越来越流行的工具。虽然大多数研究都是在受控环境中使用实验室级硬件进行的，但如果能利用家用设备，简化并扩大用户远程参与实验的规模，那将是非常理想的。研究确定了该技术目前的局限性，并对其收集的数据质量进行了评估。最后，我们提出了解决不足之处的建议，使支持眼动跟踪的远程读码研究在未来变得可行。方法：我们从 40 名在专门构建的网络应用程序上进行读码实验的参与者那里远程收集了眼动数据。所使用的眼动跟踪器在客户端工作，利用脊回归实时生成 x 和 y 坐标，预测参与者在屏幕上的注视点，无需收集和保存视频录像。我们根据软件工程眼动跟踪研究中分离眼动事件和得出指标的常用方法，对收集到的数据进行了处理和分析。由于缺乏明确用于检测低频网络摄像头数据中眼球运动固定事件的算法，我们还为此引入了一种离散阈值算法。结果：尽管进行了广泛的校准和图形用户指导，我们还是发现所收集的数据质量参差不齐。我们介绍的结果既有负面的，也有正面的，希望社会各界能从中吸取经验教训。准确度和精确度都很低，最终被认为不足以在高精度实证研究中得出有效结论。尽管如此，我们还是为确定未来研究中需要解决的关键局限性做出了贡献。除了设备、设置和配置千差万别这一总体挑战之外，我们还发现当前的网络摄像头眼动跟踪技术存在两个主要问题。第一个问题是缺乏一种经过验证的算法来分离低频数据中的固定点，从而影响了从中得出的数据的准确性。第二个问题是在预测注视位置时缺乏对头部运动的算法支持。无监督的参与者并不总能保持头部不动，即使在得到指示的情况下也是如此。因此，我们经常观察到空间移动，这破坏了许多收集到的数据集。这项研究得出了三个令人鼓舞的结论。即使发生偏移，注视点也始终分散在与刺激物的形状和大小相似的模式中，不会出现极端偏差。我们还能分辨出可识别的阅读模式。结论：准确度和精确度水平不足以对代码阅读进行逐字分析，但足以进行更广泛的粗粒度精确度研究。此外，我们还发现了影响所收集数据有效性的两个主要问题，并提供了一种固定检测算法来解决其中一个问题。如果能找到解决这些问题的合适方案，那么使用网络摄像头对代码阅读进行远程眼动跟踪研究最终将成为可能。
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Pub Date : 2024-06-03DOI: 10.1016/j.infsof.2024.107501
Austen Rainer , Claes Wohlin
Context:
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).
Objective:
To investigate the extent to which SLSs in/correctly use the term and label, “case study”, when classifying primary studies.
Methods:
We systematically collect two sub-samples (2010–2021 & 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”.
Results:
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.
Conclusions:
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.
{"title":"Reporting case studies in systematic literature studies—An evidential problem","authors":"Austen Rainer , 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 & 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}
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