Benedikt Dornauer, M. Felderer, Johannes Weinzerl, Mircea-Cristian Racasan, Martin Hess
Technical debt is often the result of Short Run decisions made during code development, which can lead to long-term maintenance costs and risks. Hence, evaluating the progression of a project and understanding related code quality aspects is essential. Fortunately, the prioritization process for addressing technical debt can be expedited with code analysis tools like the established SonarQube. Unfortunately, we experienced some limitations with this tool and have had some requirements from the industry that were not yet addressed. Through this experience report and the analysis of scientific papers, this work contributes: (1) a reassessment of technical debt within the industry, (2) considers the benefits of employing SonarQube as well as its limitations when evaluating and prioritizing technical debt, (3) introduces a novel tool named SoHist which addresses these limitations and offers additional features for the assessment and prioritization of technical debt, and (4) exemplifies the usage of this tool in two industrial settings in the ITEA3 SmartDelta project.
{"title":"SoHist: A Tool for Managing Technical Debt through Retro Perspective Code Analysis","authors":"Benedikt Dornauer, M. Felderer, Johannes Weinzerl, Mircea-Cristian Racasan, Martin Hess","doi":"10.1145/3593434.3593460","DOIUrl":"https://doi.org/10.1145/3593434.3593460","url":null,"abstract":"Technical debt is often the result of Short Run decisions made during code development, which can lead to long-term maintenance costs and risks. Hence, evaluating the progression of a project and understanding related code quality aspects is essential. Fortunately, the prioritization process for addressing technical debt can be expedited with code analysis tools like the established SonarQube. Unfortunately, we experienced some limitations with this tool and have had some requirements from the industry that were not yet addressed. Through this experience report and the analysis of scientific papers, this work contributes: (1) a reassessment of technical debt within the industry, (2) considers the benefits of employing SonarQube as well as its limitations when evaluating and prioritizing technical debt, (3) introduces a novel tool named SoHist which addresses these limitations and offers additional features for the assessment and prioritization of technical debt, and (4) exemplifies the usage of this tool in two industrial settings in the ITEA3 SmartDelta project.","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128418021","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}
[Context] Accurate time estimation is a critical aspect of predictable software engineering. Previous work shows that low source code quality increases the uncertainty in issue resolution times. [Objective] Our goal is to evaluate how developers’ project experience and file ownership are related to issue resolution times. [Method] We mine 40 proprietary software repositories and conduct an observational study. Using CodeScene, we measure source code quality and active development time connected to Jira issues. [Results] Most source code changes are made by either a marginal or dominant code owner. Also, most changes to low-quality source code are made by developers with low levels of ownership. In low-quality source code, marginal owners need 45% more time for small changes, and 93% more time for large changes. [Conclusions] Collective code ownership is a popular target, but industry practice results in many dominant and marginal owners. Marginal owners are particularly hampered when working with low-quality source code, which leads to productivity losses. In codebases plagued by technical debt, newly onboarded developers will require more time to complete tasks.
{"title":"U Owns the Code That Changes and How Marginal Owners Resolve Issues Slower in Low-Quality Source Code","authors":"Markus Borg, Adam Tornhill, Enys Mones","doi":"10.1145/3593434.3593480","DOIUrl":"https://doi.org/10.1145/3593434.3593480","url":null,"abstract":"[Context] Accurate time estimation is a critical aspect of predictable software engineering. Previous work shows that low source code quality increases the uncertainty in issue resolution times. [Objective] Our goal is to evaluate how developers’ project experience and file ownership are related to issue resolution times. [Method] We mine 40 proprietary software repositories and conduct an observational study. Using CodeScene, we measure source code quality and active development time connected to Jira issues. [Results] Most source code changes are made by either a marginal or dominant code owner. Also, most changes to low-quality source code are made by developers with low levels of ownership. In low-quality source code, marginal owners need 45% more time for small changes, and 93% more time for large changes. [Conclusions] Collective code ownership is a popular target, but industry practice results in many dominant and marginal owners. Marginal owners are particularly hampered when working with low-quality source code, which leads to productivity losses. In codebases plagued by technical debt, newly onboarded developers will require more time to complete tasks.","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116445233","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}
Code review is a common practice in software development and often conducted before code changes are merged into the code repository. A number of approaches for automatically recommending appropriate reviewers have been proposed to match such code changes to pertinent reviewers. However, such approaches are generic, i.e., they do not focus on specific types of issues during code reviews. In this paper, we propose an approach that focuses on architecture violations, one of the most critical type of issues identified during code review. Specifically, we aim at automating the recommendation of code reviewers, who are potentially qualified to review architecture violations, based on reviews of code changes. To this end, we selected three common similarity detection methods to measure the file path similarity of code commits and the semantic similarity of review comments. We conducted a series of experiments on finding the appropriate reviewers through evaluating and comparing these similarity detection methods in separate and combined ways with the baseline reviewer recommendation approach, RevFinder. The results show that the common similarity detection methods can produce acceptable performance scores and achieve a better performance than RevFinder. The sampling techniques used in recommending code reviewers can impact the performance of reviewer recommendation approaches. We also discuss the potential implications of our findings for both researchers and practitioners.
{"title":"Code Reviewer Recommendation for Architecture Violations: An Exploratory Study","authors":"Ruiyin Li, Peng Liang, P. Avgeriou","doi":"10.1145/3593434.3593450","DOIUrl":"https://doi.org/10.1145/3593434.3593450","url":null,"abstract":"Code review is a common practice in software development and often conducted before code changes are merged into the code repository. A number of approaches for automatically recommending appropriate reviewers have been proposed to match such code changes to pertinent reviewers. However, such approaches are generic, i.e., they do not focus on specific types of issues during code reviews. In this paper, we propose an approach that focuses on architecture violations, one of the most critical type of issues identified during code review. Specifically, we aim at automating the recommendation of code reviewers, who are potentially qualified to review architecture violations, based on reviews of code changes. To this end, we selected three common similarity detection methods to measure the file path similarity of code commits and the semantic similarity of review comments. We conducted a series of experiments on finding the appropriate reviewers through evaluating and comparing these similarity detection methods in separate and combined ways with the baseline reviewer recommendation approach, RevFinder. The results show that the common similarity detection methods can produce acceptable performance scores and achieve a better performance than RevFinder. The sampling techniques used in recommending code reviewers can impact the performance of reviewer recommendation approaches. We also discuss the potential implications of our findings for both researchers and practitioners.","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"3342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127496330","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}
Firas Bayram, Bestoun S. Ahmed, Erik Hallin, Anton Engman
Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, significant attention has been paid to the systematic management and construction of high-quality datasets. Nevertheless, managing voluminous and high-velocity data streams is usually performed manually (i.e. offline), making it an impractical strategy in production environments. To address this challenge, DataOps has emerged to achieve life-cycle automation of data processes using DevOps principles. However, determining the data quality based on a fitness scale constitutes a complex task within the framework of DataOps. This paper presents a novel Data Quality Scoring Operations (DQSOps) framework that yields a quality score for production data in DataOps workflows. The framework incorporates two scoring approaches, an ML prediction-based approach that predicts the data quality score and a standard-based approach that periodically produces the ground-truth scores based on assessing several data quality dimensions. We deploy the DQSOps framework in a real-world industrial use case. The results show that DQSOps achieves significant computational speedup rates compared to the conventional approach of data quality scoring while maintaining high prediction performance.
{"title":"DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications","authors":"Firas Bayram, Bestoun S. Ahmed, Erik Hallin, Anton Engman","doi":"10.1145/3593434.3593445","DOIUrl":"https://doi.org/10.1145/3593434.3593445","url":null,"abstract":"Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, significant attention has been paid to the systematic management and construction of high-quality datasets. Nevertheless, managing voluminous and high-velocity data streams is usually performed manually (i.e. offline), making it an impractical strategy in production environments. To address this challenge, DataOps has emerged to achieve life-cycle automation of data processes using DevOps principles. However, determining the data quality based on a fitness scale constitutes a complex task within the framework of DataOps. This paper presents a novel Data Quality Scoring Operations (DQSOps) framework that yields a quality score for production data in DataOps workflows. The framework incorporates two scoring approaches, an ML prediction-based approach that predicts the data quality score and a standard-based approach that periodically produces the ground-truth scores based on assessing several data quality dimensions. We deploy the DQSOps framework in a real-world industrial use case. The results show that DQSOps achieves significant computational speedup rates compared to the conventional approach of data quality scoring while maintaining high prediction performance.","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436076","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}
GitHub Actions is a powerful tool for automating workflows on GitHub repositories, with thousands of Actions currently available on the GitHub Marketplace. So far, the research community has conducted mining studies on Actions, with much of the focus on CI/CD. However, the motivation and best practices of developers for using, developing, and debugging Actions are unknown. To address this gap, we conducted a survey study with 90 Action users and developers. Our findings indicate that developers prefer Actions with verified creators and more stars when choosing between similar Actions, and often switch to alternative Actions when faced with bugs or a lack of documentation. We also found that developers find the composition of YAML files, which are essential for Action integration, challenging and error-prone. They primarily rely on Q&A forums to fix issues with these YAML files. Finally, we observed that developers would not likely adopt Actions when there are concerns around complexity and security risks. Our study summarizes developers’ perceptions, decision-making process, and challenges in using, developing, and debugging Actions. We provide recommendations for improving the visibility, re-usability, documentation, and support surrounding GitHub Actions.
{"title":"Developers’ Perception of GitHub Actions: A Survey Analysis","authors":"Sk Golam Saroar, Maleknaz Nayebi","doi":"10.1145/3593434.3593475","DOIUrl":"https://doi.org/10.1145/3593434.3593475","url":null,"abstract":"GitHub Actions is a powerful tool for automating workflows on GitHub repositories, with thousands of Actions currently available on the GitHub Marketplace. So far, the research community has conducted mining studies on Actions, with much of the focus on CI/CD. However, the motivation and best practices of developers for using, developing, and debugging Actions are unknown. To address this gap, we conducted a survey study with 90 Action users and developers. Our findings indicate that developers prefer Actions with verified creators and more stars when choosing between similar Actions, and often switch to alternative Actions when faced with bugs or a lack of documentation. We also found that developers find the composition of YAML files, which are essential for Action integration, challenging and error-prone. They primarily rely on Q&A forums to fix issues with these YAML files. Finally, we observed that developers would not likely adopt Actions when there are concerns around complexity and security risks. Our study summarizes developers’ perceptions, decision-making process, and challenges in using, developing, and debugging Actions. We provide recommendations for improving the visibility, re-usability, documentation, and support surrounding GitHub Actions.","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248051","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}
Aakash Ahmad, Muhammad Waseem, Peng Liang, M. Fahmideh, Mst Shamima Aktar, T. Mikkonen
Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders’ perspectives, designers’ intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. Software Development Bots (DevBots) trained on large language models can help synergise architects’ knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects’ productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.
{"title":"Towards Human-Bot Collaborative Software Architecting with ChatGPT","authors":"Aakash Ahmad, Muhammad Waseem, Peng Liang, M. Fahmideh, Mst Shamima Aktar, T. Mikkonen","doi":"10.1145/3593434.3593468","DOIUrl":"https://doi.org/10.1145/3593434.3593468","url":null,"abstract":"Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders’ perspectives, designers’ intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. Software Development Bots (DevBots) trained on large language models can help synergise architects’ knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects’ productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121582063","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":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","authors":"","doi":"10.1145/3593434","DOIUrl":"https://doi.org/10.1145/3593434","url":null,"abstract":"","PeriodicalId":178596,"journal":{"name":"Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121394010","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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