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Can github issues Help in App Review Classifications?

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第8期33卷 1-42页

作者： Abedini, Yasaman Heydarnoori, Abbas Sharif Univ Technol Dept Comp Engn Tehran Iran Bowling Green State Univ Dept Comp Sci Bowling Green OH USA

App reviews reflect various user requirements that can aid in planning maintenance tasks. Recently, proposed approaches for automatically classifying user reviews rely on machine learning algorithms. A previous study demonstrated that models trained on existing labeled datasets exhibit poor performance when predicting new ones. Therefore, a comprehensive labeled dataset is essential to train a more precise model. In this paper, we propose a novel approach that assists in augmenting labeled datasets by utilizing information extracted from an additional source, github issues, that contains valuable information about user requirements. First, we identify issues concerning review intentions (bug reports, feature requests, and others) by examining the issue labels. Then, we analyze issue bodies and define 19 language patterns for extracting targeted information. Finally, we augment the manually labeled review dataset with a subset of processed issues through the Within- App, Within-Context, and Between-App Analysis methods. We conducted several experiments to evaluate the proposed approach. Our results demonstrate that using labeled issues for data augmentation can improve the F1-score to 6.3 in bug reports and 7.2 in feature requests. Furthermore, we identify an effective range of 0.3 to 0.7 for the auxiliary volume, which provides better performance improvements.

关键词： App Review Classification github issues Intention Mining Machine learning

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Detecting Security Requirements in github issues - Novel Dataset and SmallBERT-based model 14

Detecting Security Requirements in GitHub Issues - Novel Dat...

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14th IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Minina, Polina Sadovykh, Andrey Ivanov, Vladimir SOFTEAM Paris France

ISBN: (纸本)9798350339826

Cloud application security initiates with the analysis of security requirements in DevOps. This involves gathering, managing, and tracking requirements within integrated issue-tracking systems found in repositories like github. DevOps offers advantages in cloud app development, such as accelerated deployment, improved collaboration, and enhanced reliability. In DevOps, while many security verification tools are automated, security requirements analysis often relies on manual procedures. User feedback plays a pivotal role in shaping cloud application requirements, and the industry actively seeks automation solutions to expedite development. Prior research has demonstrated the limited performance of conventional NLP models trained on established datasets, such as PROMISE, when employed in the context of github issues. Recent studies have explored the integration of deep learning, particularly leveraging modern large language models and transfer learning architectures, to address requirements engineering challenges. However, a significant issue persists - the transferability of these models. While these models excel when applied to datasets similar to those they were trained on, their performance often drastically falls when dealing with external domains. In our paper, we introduce an automated method for classifying requirements within issue trackers. This method utilizes a novel dataset comprising 12,000 security and non-security issues collected from open github repositories. We employed a SmallBERT-based model for training and conducted a series of experiments. Our research reaffirms the challenge related to the transferability of NLP models. Simultaneously, our model yields highly promising results when applied to github issues, even in challenging scenarios involving issues from projects that were not part of the training dataset and structured requirements texts from the PROMISE dataset. In summary, our approach significantly contributes to enhancing DevOps prac

关键词： Security requirements github issues NLP Classification Dataset Machine Learning Deep learning BERT

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Visualizing github issues

Visualizing GitHub Issues

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Working Conference on Software Visualization (VISSOFT)

作者： Fiechter, Aron Minelli, Roberto Nagy, Csaba Lanza, Michele REVEAL Software Inst USI Lugano Switzerland

ISBN: (纸本)9781665431446

The rise of distributed version control systems, such as git, and platforms built on top of it, such as github, has triggered a change in how software is developed. Most notably, state-of-the-art practice foresees the use of pull requests and issues, enriched by means to enable discussions among the involved people. Platforms like github and GitLab have thus turned into comprehensive and cohesive modern software development environments, also offering additional mechanisms, such as code review tools and a transversal support for continuous integration and deployment. However, the plethora of concepts, mechanisms, and their interconnections are stored and presented in textual form, which makes the understanding of the underlying evolutionary processes difficult. We introduce the notion of an issue tale, a visual narrative of the events and actors revolving around any github issue, and present an approach, implemented as an interactive visual analytics tool, to depict and analyze the relevant information pertaining to issue tales. We illustrate our approach and its implementation on several open-source software systems.

关键词： software evolution visualization visual analytics github issues

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Towards Prioritizing github issues 2020

Towards Prioritizing GitHub Issues

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13th Innovations in Software Engineering Conference (ISEC)

作者： Dhasade, Akash Balasaheb Venigalla, Akhila Sri Manasa Chimalakonda, Sridhar Indian Inst Technol Res Intelligent Software & Human Analyt RISHA Lab Tirupati Andhra Pradesh India

ISBN: (纸本)9781450375948

The vast growth in usage of github by developers to host their projects has led to extensive forking and open source contributions. These contributions occur in the form of issues that report bugs or pull requests to either fix bugs or add newfeatures to the project. On the other hand, massive increase in the number of issues reported by developers and users is a major challenge for integrators, as the number of concurrent issues to be handled is much higher than the number of core contributors. While there exists prior work on prioritizing pull requests, in this paper we make an attempt towards prioritizing issues using machine learning techniques. We present the Issue Prioritizer, a tool to prioritize issues based on three criteria: issue lifetime, issue hotness and category of the issue. We see this work as an initial step towards supporting developers to handle large volumes of issues in projects.

关键词： Automatic Issue Prioritisation github issues Multiple Concurrent issues Priority Ranking Dynamic Tracking

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The role of surprisal in issue trackers

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EMPIRICAL SOFTWARE ENGINEERING 2025年第1期30卷 1-34页

作者： Caddy, James Treude, Christoph Wagner, Markus Barr, Earl T. Univ Adelaide Adelaide Australia Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore Monash Univ Fac Informat Technol Melbourne Australia UCL Dept Comp Sci London England

ContextSoftware development creates and relies on a large volume of information, yet the volume of this information can make it challenging for developers to maintain an overview of all goings-on that a team and external actors contribute to a project. We posit that unexpected or "surprising" events could serve as important signposts amidst this information overload. These unexpected events may indicate underlying anomalies or emergent situations that require immediate attention. To explore this premise, our study leverages the concept of 'surprisal' from information theory to identify and quantify these unusual occurrences from the issues and pull requests of popular open-source software *** from a previously published research protocol, our study investigates whether a correlation exists between the 'surprisal' of issues and their perceived importance or difficulty within software *** performed a comprehensive analysis of approximately two million issues and pull requests, gathered from 1,270 repositories. Their 'surprisal' was then examined in relation to several indicative metrics of difficulty and perceived importance. Our results indicate only a weak correlation. This outcome underscores the need for further research to devise more effective strategies for helping developers prioritise issues.

关键词： Self-information n-gram github issues

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Exploring the problems, their causes and solutions of AI pair programming: A study on github and Stack Overflow

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 219卷

作者： Zhou, Xiyu Liang, Peng Zhang, Beiqi Li, Zengyang Ahmad, Aakash Shahin, Mojtaba Waseem, Muhammad Wuhan Univ Sch Comp Sci Wuhan Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smart Wuhan Peoples R China Lancaster Univ Leipzig Sch Comp & Commun Leipzig Germany RMIT Univ Sch Comp Technol Melbourne Australia Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland

With the recent advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), AI-based code generation tools become a practical solution for software development. github Copilot, the AI pair programmer, utilizes machine learning models trained on a large corpus of code snippets to generate code suggestions using natural language processing. Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot. To this end, we conducted an empirical study to understand the problems that practitioners face when using Copilot, as well as their underlying causes and potential solutions. We collected data from 473 github issues, 706 github discussions, and 142 Stack Overflow posts. Our results reveal that (1) Operation Issue and Compatibility Issue are the most common problems faced by Copilot users, (2) Copilot Internal Error, Network Connection Error, and Editor/IDE Compatibility Issue are identified as the most frequent causes, and (3) Bug Fixed by Copilot, Modify Configuration/Setting, and Use Suitable Version are the predominant solutions. Based on the results, we discuss the potential areas of Copilot for enhancement, and provide the implications for the Copilot users, the Copilot team, and researchers.

关键词： github copilot github issues github discussions StackOverflow post Problem Cause Solution

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An Empirical Analysis of Issue Templates Usage in Large-Scale Projects on github

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第5期33卷 1-28页

作者： Sulun, Emre Sacakci, Metehan Tuzun, Eray Bilkent Univ TR-06800 Bilkent Turkiye

github issues is a widely used issue tracking tool in open-source software projects. Originally designed with broad flexibility, its lack of standardization led to incomplete issue reports, impeding software development and maintenance efficiency. To counteract this, github introduced issue templates in 2016, which rapidly became popular. Our study assesses the current use and evolution of these templates in large-scale open-source projects and their impact on issue tracking metrics, including resolution time, number of reopens, and number of issue comments. Employing a comprehensive analysis of 350 templates from 100 projects, we also evaluated over 1.9 million issues for template conformity and impact. Additionally, we solicited insights from open-source software maintainers through a survey. Our findings highlight issue templates' extensive usage in 99 of the 100 surveyed projects, with a growing preference for YAML-based templates, a more structured template variant. Projects with a template exhibited markedly reduced resolution time (381.02 days to 103.18 days) and reduced issue comment count (4.95 to 4.32) compared to those without. The use of YAML-based templates further significantly decreased resolution time, the number of reopenings, and the discussion extent. Thus, our research underscores issue templates' positive impact on large-scale open-source projects, offering recommendations for improved effectiveness.

关键词： Issue templates issue forms issue tracking github issues bug tracking empirical study

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Incivility detection in open source code review and issue discussions

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 209卷

作者： Ferreira, Isabella Rafiq, Ahlaam Cheng, Jinghui Polytech Montreal Dept Comp & Software Engn Montreal PQ Canada Indian Inst Technol Guwahati Dept Phys Gauhati Assam India

Given the democratic nature of open source development, code review and issue discussions may be uncivil. Incivility, defined as features of discussion that convey an unnecessarily disrespectful tone, can have negative consequences to open source communities. To prevent or minimize these negative consequences, open source platforms have included mechanisms for removing uncivil language from the discussions. However, such approaches require manual inspection, which can be overwhelming given the large number of discussions. To help open source communities deal with this problem, in this paper, we aim to compare six classical machine learning models with BERT to detect incivility in open source code review and issue discussions. Furthermore, we assess if adding contextual information in the previous email/comment improves the models' performance and how well the models perform in a cross-platform setting. We found that BERT performs better than classical machine learning models, with a best F1-score of 0.95. Furthermore, classical machine learning models tend to underperform to detect tone-bearing and civil discussions. Our results show that adding the previous email/comment to BERT did not improve its performance and that none of the analyzed classifiers had an outstanding performance in a cross-platform setting. Finally, we provide insights into the tones that the classifiers misclassify and lessons learned for using automated techniques in incivility detection.

关键词： Incivility Code review github issues Open source Bert Machine learning

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Prioritising github Priority Labels 20

Prioritising GitHub Priority Labels

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20th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE)

作者： Caddy, James Treude, Christoph Univ Adelaide Adelaide SA Australia Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798400706752

Communities on github often use issue labels as a way of triaging issues by assigning them priority ratings based on how urgently they should be addressed. The labels used are determined by the repository contributors and not standardised by github. This makes it difficult for priority-related reasoning across repositories for both researchers and contributors. Previous work shows interest in how issues are labelled and what the consequences for those labels are. For instance, some previous work has used clustering models and natural language processing to categorise labels without a particular emphasis on priority. With this publication, we introduce a unique data set of 812 manually categorised labels pertaining to priority;normalised and ranked as low-, medium-, or high-priority. To provide an example of how this data set could be used, we have created a tool for github contributors that will create a list of the highest priority issues from the repositories to which they contribute. We have released the data set and the tool for anyone to use on Zenodo because we hope that this will help the open source community address high-priority issues more effectively and inspire other uses.

关键词： github issues task priority data sets

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Unveiling Quality in Chatbot Conversations: Quantitative Analysis of Chatbot Requirements 23

Unveiling Quality in Chatbot Conversations: Quantitative Ana...

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22nd Brazilian Symposium on Software Quality (SBQS)

作者： Sousa Silva, Geovana Ramos Canedo, Edna Dias Univ Brasilia UnB Dept Comp Sci Brasilia DF Brazil

ISBN: (纸本)9798400707865

As conversational assistants and natural language interfaces proliferate, the demand for a precise understanding of quality software requirements for chatbots becomes increasingly critical. In this work, we adopted a quantitative methodology, scrutinizing a dataset composed of conversational requirements from a diverse range of agile projects for chatbot development, and identified meaningful patterns in the language and structure utilized. Our investigation led to significant findings, revealing the importance of structured documentation, conversation flow, and user interaction in the development of chatbots, with the most desired quality attributes being capability, naturalness, straightforwardness, and clarity. In addition, a significant emphasis was placed on feature development and meeting acceptance criteria. The research also illuminated the iterative nature of chatbot development, with a recurrent presence of verbs related to improvement or refactoring. While less pronounced, the roles of documentation and testing in ensuring chatbot quality and effectiveness were also noted. This work provides valuable insights into chatbot requirements management and the significance of quality attributes in chatbot development.

关键词： chatbot quality attributes software requirements github issues open source

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