Pub Date : 2023-04-15DOI: 10.1109/SERA57763.2023.10197828
Md Athikul Islam, Rizbanul Hasan, Nasir U. Eisty
Context: Agile development methodologies in the software industry have increased significantly over the past decade. Although one of the main aspects of agile software development (ASD) is less documentation, there have always been conflicting opinions about what to document in ASD. Objective: This study aims to systematically identify what to document in ASD, which documentation tools and methods are in use, and how those tools can overcome documentation challenges. Method: We performed a systematic literature review of the studies published between 2010 and June 2021 that discusses agile documentation. Then, we systematically selected a pool of 74 studies using particular inclusion and exclusion criteria. After that, we conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: We found nine primary vital factors to add to agile documentation from our pool of studies. Our analysis shows that agile practitioners have primarily developed their documentation tools and methods focusing on these factors. The results suggest that the tools and techniques in agile documentation are not in sync, and they separately solve different challenges. Conclusions: Based on our results and discussion, researchers and practitioners will better understand how current agile documentation tools and practices perform. In addition, investigation of the synchronization of these tools will be helpful in future research and development.
{"title":"Documentation Practices in Agile Software Development: A Systematic Literature Review","authors":"Md Athikul Islam, Rizbanul Hasan, Nasir U. Eisty","doi":"10.1109/SERA57763.2023.10197828","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197828","url":null,"abstract":"Context: Agile development methodologies in the software industry have increased significantly over the past decade. Although one of the main aspects of agile software development (ASD) is less documentation, there have always been conflicting opinions about what to document in ASD. Objective: This study aims to systematically identify what to document in ASD, which documentation tools and methods are in use, and how those tools can overcome documentation challenges. Method: We performed a systematic literature review of the studies published between 2010 and June 2021 that discusses agile documentation. Then, we systematically selected a pool of 74 studies using particular inclusion and exclusion criteria. After that, we conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: We found nine primary vital factors to add to agile documentation from our pool of studies. Our analysis shows that agile practitioners have primarily developed their documentation tools and methods focusing on these factors. The results suggest that the tools and techniques in agile documentation are not in sync, and they separately solve different challenges. Conclusions: Based on our results and discussion, researchers and practitioners will better understand how current agile documentation tools and practices perform. In addition, investigation of the synchronization of these tools will be helpful in future research and development.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121286608","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 : 2023-04-04DOI: 10.1109/SERA57763.2023.10197836
Bishal Lakha, Kalyan Bhetwal, Nasir U. Eisty
Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.
{"title":"Analysis of Software Engineering Practices in General Software and Machine Learning Startups","authors":"Bishal Lakha, Kalyan Bhetwal, Nasir U. Eisty","doi":"10.1109/SERA57763.2023.10197836","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197836","url":null,"abstract":"Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418628","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 : 2023-03-29DOI: 10.1109/SERA57763.2023.10197835
Patrick Chadbourne, Nasir U. Eisty
Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.
{"title":"Applications of Causality and Causal Inference in Software Engineering","authors":"Patrick Chadbourne, Nasir U. Eisty","doi":"10.1109/SERA57763.2023.10197835","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197835","url":null,"abstract":"Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and various fields of software engineering. This paper aims to uncover which software engineering fields are currently benefiting from the study of causal inference and causal reasoning, as well as which aspects of various problems are best addressed using this methodology. With this information, this paper also aims to find future subjects and fields that would benefit from this form of reasoning and to provide that information to future researchers. This paper follows a systematic literature review, including; the formulation of a search query, inclusion and exclusion criteria of the search results, clarifying questions answered by the found literature, and synthesizing the results from the literature review. Through close examination of the 45 found papers relevant to the research questions, it was revealed that the majority of causal reasoning as related to software engineering is related to testing through root cause localization. Furthermore, most causal reasoning is done informally through an exploratory process of forming a Causality Graph as opposed to strict statistical analysis or introduction of interventions. Finally, causal reasoning is also used as a justification for many tools intended to make the software more human-readable by providing additional causal information to logging processes or modeling languages.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"28 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125686610","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 : 2023-03-29DOI: 10.1109/SERA57763.2023.10197778
Jeffrey Fairbanks, Akshharaa Tharigonda, Nasir U. Eisty
Numerous articles emphasize the benefits of implementing Continuous Integration and Delivery (CI/CD) pipelines in software development. These pipelines are expected to improve a project’s reputation and decrease the number of commits and issues in the repository. Although CI/CD adoption may be slow initially, it is believed to accelerate service delivery and deployment in the long run. This study aims to investigate the impact of CI/CD on commit velocity and issue counts in two open-source repositories, GitLab and GitHub. By analyzing more than 12,000 repositories and recording every commit and issue, it was discovered that CI/CD enhances commit velocity by 141.19% but also increases the number of issues by 321.21%.
{"title":"Analyzing the Effects of CI/CD on Open Source Repositories in GitHub and GitLab","authors":"Jeffrey Fairbanks, Akshharaa Tharigonda, Nasir U. Eisty","doi":"10.1109/SERA57763.2023.10197778","DOIUrl":"https://doi.org/10.1109/SERA57763.2023.10197778","url":null,"abstract":"Numerous articles emphasize the benefits of implementing Continuous Integration and Delivery (CI/CD) pipelines in software development. These pipelines are expected to improve a project’s reputation and decrease the number of commits and issues in the repository. Although CI/CD adoption may be slow initially, it is believed to accelerate service delivery and deployment in the long run. This study aims to investigate the impact of CI/CD on commit velocity and issue counts in two open-source repositories, GitLab and GitHub. By analyzing more than 12,000 repositories and recording every commit and issue, it was discovered that CI/CD enhances commit velocity by 141.19% but also increases the number of issues by 321.21%.","PeriodicalId":211080,"journal":{"name":"2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129923852","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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