R. Ouriques, Fabian Fagerholm, Daniel Mendez, Baldvin Gislason Bern
Context: Boundary Artefacts (BAs) support software development activities in many aspects because it carries lots of information in the same object that can be used and interpreted by several social groups within an organisation. When the BAs are inconsistent regarding their content, such as many meanings or lack of contextual information, their efficiency is reduced because stakeholders will not trust them. Objective: This study aimed to understand the implications of differences in the perception of trust on software projects and their influence on stakeholders’ behaviour. Methods: We conducted an exploratory case study to observe the creation and utilisation of one specific BA and the implications of differences in trust and their influence on stakeholders’ behaviour. Results : Our investigation has shown that practitioners adding and adjusting existing content do not entirely understand the stakeholders’ needs. Together with the partial management of the content, trust is impacted. When the content of BAs does not meet the trust factors, specifically reliability and predictability, the stakeholders cannot execute their tasks appropriately, and several implications affect the software development project. Additionally, they create workarounds to supply their needs. Conclusion: The differences in trust in BAs affect software projects in different areas of the organisation and interfere with the task execution of various stakeholders. The decrease in trust results from inconsistencies in the content associated with the lack of management of the BA. A structured strategy for representing and managing a BA’s content seems appropriate to increase trust levels and efficiency.
{"title":"An investigation of causes and effects of trust in Boundary Artefacts","authors":"R. Ouriques, Fabian Fagerholm, Daniel Mendez, Baldvin Gislason Bern","doi":"10.2139/ssrn.4253491","DOIUrl":"https://doi.org/10.2139/ssrn.4253491","url":null,"abstract":"Context: Boundary Artefacts (BAs) support software development activities in many aspects because it carries lots of information in the same object that can be used and interpreted by several social groups within an organisation. When the BAs are inconsistent regarding their content, such as many meanings or lack of contextual information, their efficiency is reduced because stakeholders will not trust them. Objective: This study aimed to understand the implications of differences in the perception of trust on software projects and their influence on stakeholders’ behaviour. Methods: We conducted an exploratory case study to observe the creation and utilisation of one specific BA and the implications of differences in trust and their influence on stakeholders’ behaviour. Results : Our investigation has shown that practitioners adding and adjusting existing content do not entirely understand the stakeholders’ needs. Together with the partial management of the content, trust is impacted. When the content of BAs does not meet the trust factors, specifically reliability and predictability, the stakeholders cannot execute their tasks appropriately, and several implications affect the software development project. Additionally, they create workarounds to supply their needs. Conclusion: The differences in trust in BAs affect software projects in different areas of the organisation and interfere with the task execution of various stakeholders. The decrease in trust results from inconsistencies in the content associated with the lack of management of the BA. A structured strategy for representing and managing a BA’s content seems appropriate to increase trust levels and efficiency.","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"466 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134261627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"COSMOS: A comprehensive framework for automatically generating domain-oriented test suite","authors":"A. Kalaee, S. Parsa, Negar Fathi","doi":"10.2139/ssrn.4092941","DOIUrl":"https://doi.org/10.2139/ssrn.4092941","url":null,"abstract":"","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132863484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-09DOI: 10.48550/arXiv.2301.03554
Saara Tenhunen, T. Männistö, Matti Luukkainen, Petri Ihantola
Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. In a systematic literature review for 2007-2022, we identified 127 primary studies. These studies were analysed based on their presented course characteristics and the reported course outcomes. The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4-5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses also offer versatile assessments for students throughout the project. This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We further synthesise insights on the reported outcomes of capstone courses. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.
{"title":"A systematic literature review of capstone courses in software engineering","authors":"Saara Tenhunen, T. Männistö, Matti Luukkainen, Petri Ihantola","doi":"10.48550/arXiv.2301.03554","DOIUrl":"https://doi.org/10.48550/arXiv.2301.03554","url":null,"abstract":"Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. In a systematic literature review for 2007-2022, we identified 127 primary studies. These studies were analysed based on their presented course characteristics and the reported course outcomes. The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4-5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses also offer versatile assessments for students throughout the project. This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We further synthesise insights on the reported outcomes of capstone courses. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124452210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.48550/arXiv.2301.09531
V. Cortellessa, Daniele Di Pompeo, Vincenzo Stoico, Michele Tucci
Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42%) while preserving or even improving the reliability (by up to 32%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. We can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort.
{"title":"Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models","authors":"V. Cortellessa, Daniele Di Pompeo, Vincenzo Stoico, Michele Tucci","doi":"10.48550/arXiv.2301.09531","DOIUrl":"https://doi.org/10.48550/arXiv.2301.09531","url":null,"abstract":"Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42%) while preserving or even improving the reliability (by up to 32%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. We can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort.","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130195832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A light-weight data augmentation method for fault localization","authors":"Jian Hu, Huan Xie, Yan Lei, Ke Yu","doi":"10.2139/ssrn.4182106","DOIUrl":"https://doi.org/10.2139/ssrn.4182106","url":null,"abstract":"","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129661278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-20DOI: 10.48550/arXiv.2212.10693
Khlood Ahmad, Mohamed Almorsy, Chetan Arora, Muneera Bano, John C. Grundy
[Context] In traditional software systems, Requirements Engineering (RE) activities are well-established and researched. However, building Artificial Intelligence (AI) based software with limited or no insight into the system's inner workings poses significant new challenges to RE. Existing literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). [Objective] This paper investigates current approaches for specifying requirements for AI systems, identifies available frameworks, methodologies, tools, and techniques used to model requirements, and finds existing challenges and limitations. [Method] We performed a systematic mapping study to find papers on current RE4AI approaches. We identified 43 primary studies and analysed the existing methodologies, models, tools, and techniques used to specify and model requirements in real-world scenarios. [Results] We found several challenges and limitations of existing RE4AI practices. The findings highlighted that current RE applications were not adequately adaptable for building AI systems and emphasised the need to provide new techniques and tools to support RE4AI. [Conclusion] Our results showed that most of the empirical studies on RE4AI focused on autonomous, self-driving vehicles and managing data requirements, and areas such as ethics, trust, and explainability need further research.
{"title":"Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study","authors":"Khlood Ahmad, Mohamed Almorsy, Chetan Arora, Muneera Bano, John C. Grundy","doi":"10.48550/arXiv.2212.10693","DOIUrl":"https://doi.org/10.48550/arXiv.2212.10693","url":null,"abstract":"[Context] In traditional software systems, Requirements Engineering (RE) activities are well-established and researched. However, building Artificial Intelligence (AI) based software with limited or no insight into the system's inner workings poses significant new challenges to RE. Existing literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). [Objective] This paper investigates current approaches for specifying requirements for AI systems, identifies available frameworks, methodologies, tools, and techniques used to model requirements, and finds existing challenges and limitations. [Method] We performed a systematic mapping study to find papers on current RE4AI approaches. We identified 43 primary studies and analysed the existing methodologies, models, tools, and techniques used to specify and model requirements in real-world scenarios. [Results] We found several challenges and limitations of existing RE4AI practices. The findings highlighted that current RE applications were not adequately adaptable for building AI systems and emphasised the need to provide new techniques and tools to support RE4AI. [Conclusion] Our results showed that most of the empirical studies on RE4AI focused on autonomous, self-driving vehicles and managing data requirements, and areas such as ethics, trust, and explainability need further research.","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131806638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.48550/arXiv.2212.07763
Ava Heinonen, Bettina Lehtelä, Arto Hellas, Fabian Fagerholm
Programmers' mental models represent their knowledge and understanding of programs, programming concepts, and programming in general. They guide programmers' work and influence their task performance. Understanding mental models is important for designing work systems and practices that support programmers. Although the importance of programmers' mental models is widely acknowledged, research on mental models has decreased over the years. The results are scattered and do not take into account recent developments in software engineering. We analyze the state of research into programmers' mental models and provide an overview of existing research. We connect results on mental models from different strands of research to form a more unified knowledge base on the topic. We conducted a systematic literature review on programmers' mental models. We analyzed literature addressing mental models in different contexts, including mental models of programs, programming tasks, and programming concepts. Using nine search engines, we found 3678 articles (excluding duplicates). 84 were selected for further analysis. Using the snowballing technique, we obtained a final result set containing 187 articles. We show that the literature shares a kernel of shared understanding of mental models. By collating and connecting results on mental models from different fields of research, we uncovered some well-researched aspects, which we argue are fundamental characteristics of programmers' mental models. This work provides a basis for future work on mental models. The research field on programmers' mental models still faces many challenges rising from a lack of a shared knowledge base and poorly defined constructs. We created a unified knowledge base on the topic. We also point to directions for future studies. In particular, we call for studies that examine programmers working with modern practices and tools.
{"title":"Synthesizing Research on Programmers' Mental Models of Programs, Tasks and Concepts - a Systematic Literature Review","authors":"Ava Heinonen, Bettina Lehtelä, Arto Hellas, Fabian Fagerholm","doi":"10.48550/arXiv.2212.07763","DOIUrl":"https://doi.org/10.48550/arXiv.2212.07763","url":null,"abstract":"Programmers' mental models represent their knowledge and understanding of programs, programming concepts, and programming in general. They guide programmers' work and influence their task performance. Understanding mental models is important for designing work systems and practices that support programmers. Although the importance of programmers' mental models is widely acknowledged, research on mental models has decreased over the years. The results are scattered and do not take into account recent developments in software engineering. We analyze the state of research into programmers' mental models and provide an overview of existing research. We connect results on mental models from different strands of research to form a more unified knowledge base on the topic. We conducted a systematic literature review on programmers' mental models. We analyzed literature addressing mental models in different contexts, including mental models of programs, programming tasks, and programming concepts. Using nine search engines, we found 3678 articles (excluding duplicates). 84 were selected for further analysis. Using the snowballing technique, we obtained a final result set containing 187 articles. We show that the literature shares a kernel of shared understanding of mental models. By collating and connecting results on mental models from different fields of research, we uncovered some well-researched aspects, which we argue are fundamental characteristics of programmers' mental models. This work provides a basis for future work on mental models. The research field on programmers' mental models still faces many challenges rising from a lack of a shared knowledge base and poorly defined constructs. We created a unified knowledge base on the topic. We also point to directions for future studies. In particular, we call for studies that examine programmers working with modern practices and tools.","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127678797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.48550/arXiv.2212.01011
Wen-Yen Wang, Chenhao Wu, Jie He
Automated bug priority inference can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. Currently, there are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports; 2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. In this paper, we propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for bug priority inference in a supervised way. To verify the effectiveness of CLeBPI, we choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its effectiveness.
{"title":"CLeBPI: Contrastive Learning for Bug Priority Inference","authors":"Wen-Yen Wang, Chenhao Wu, Jie He","doi":"10.48550/arXiv.2212.01011","DOIUrl":"https://doi.org/10.48550/arXiv.2212.01011","url":null,"abstract":"Automated bug priority inference can reduce the time overhead of bug triagers for priority assignments, improving the efficiency of software maintenance. Currently, there are two orthogonal lines for this task, i.e., traditional machine learning based (TML-based) and neural network based (NN-based) approaches. Although these approaches achieve competitive performance, our observation finds that existing approaches face the following two issues: 1) TML-based approaches require much manual feature engineering and cannot learn the semantic information of bug reports; 2) Both TML-based and NN-based approaches cannot effectively address the label imbalance problem because they are difficult to distinguish the semantic difference between bug reports with different priorities. In this paper, we propose CLeBPI (Contrastive Learning for Bug Priority Inference), which leverages pre-trained language model and contrastive learning to tackle the above-mentioned two issues. Specifically, CLeBPI is first pre-trained on a large-scale bug report corpus in a self-supervised way, thus it can automatically learn contextual representations of bug reports without manual feature engineering. Afterward, it is further pre-trained by a contrastive learning objective, which enables it to distinguish semantic differences between bug reports, learning more precise contextual representations for each bug report. When finishing pre-training, we can connect a classification layer to CLeBPI and fine-tune it for bug priority inference in a supervised way. To verify the effectiveness of CLeBPI, we choose four baseline approaches and conduct comparison experiments on a public dataset. The experimental results show that CLeBPI outperforms all baseline approaches by 23.86%-77.80% in terms of weighted average F1-score, showing its effectiveness.","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116718287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Knowledge diffusion trajectories of agile software development research: A main path analysis","authors":"Yulianus Palopak, Sun-Jen Huang, W. Ratnasari","doi":"10.2139/ssrn.4102951","DOIUrl":"https://doi.org/10.2139/ssrn.4102951","url":null,"abstract":"","PeriodicalId":133352,"journal":{"name":"Inf. Softw. Technol.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121534248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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