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12th International Conference on Information and Communication Technology and system (ICTS)

作者： Phetrungnapha, Kittisak Senivongse, Twittie Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781728121338

Mobile application development has now been in the mainstream and a lot of mobile applications have been released to Apple App Store and GooglePlay Store. As there are many applications in the same category and the competition is very high in the market, mobile development teams need to get user feedback on their applications so that they can improve quality of the applications. An important source of feedback is user review on App Store and Play Store from which the developers can analyze problems and recommendations for future maintenance and evolution of the applications. Since there might be a large number of user reviews for each release of a mobile application, this paper proposes an automated approach to classifying user reviews as bug reports or feature requests. These classification results are used as a basis for generating tickets for an issue tracking system, i.e. Jira. In user review classification, text classification, natural language processing, sentiment analysis, and review metadata are used with several machine learning algorithms, i.e. Naive Bayes, Decision Tree, KNN, LinearSVC, Logistic Regression, and Ensemble methods. The best classifiers for both categories of reviews are used further in an implementation of a Jira ticket generating tool. The tool considers semantic similarity of review comments and can filter out duplicate user reviews. When necessary, the tool applies text summarization to the user review to extract the title of a ticket for the corresponding bug report or feature request. The approach can facilitate the mobile development team in their maintenance task as the developers can pick the tickets and work on them to improve the application in a future release.

关键词： user reviews issue tracking system text classification text similarity text summarization machine learning natural language processing

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How Much Undocumented Knowledge is there in Agile Software Development? Case Study on Industrial Project using issue tracking system and Version Control system 25

How Much Undocumented Knowledge is there in Agile Software D...

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25th IEEE International Requirements Engineering Conference (RE)

作者： Saito, Shinobu Iimura, Yukako Massey, Aaron K. Anton, Annie I. NTT Corp Software Innovat Ctr Tokyo Japan Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA

ISBN: (纸本)9781538631911

In agile software development projects, software engineers prioritize implementation over documentation to eliminate needless documentation. Is the cost of missing documentation greater than the cost of producing unnecessary or unused documentation? Even without these documents, software engineers maintain other software artifacts, such as tickets in an issue tracking system (ITS) or source code committed to a version control system (VCS). Do these artifacts contain the necessary knowledge? In this paper, we examine undocumented knowledge in an agile software development project at NTT. For our study, we collected 159 commit logs in a VCS and 102 tickets in the ITS from the three-month period of the project. We propose a ticket-commit network chart (TCC) that visually represents time-series commit activities along with filed issue tickets. We also implement a tool to generate the TCC using both commit log and ticket data. Our study revealed that in 16% of all commits, software engineers committed source code to the VCS without a corresponding issue ticket in the ITS. Had these commits been based on individual issue tickets, these "unissued" tickets would have accounted for 20% of all tickets. Software users and requirements engineers also evaluated the contents of these commits and found that 42% of the "unissued" tickets were required for software operation and 23% of those were required for requirements modification.

关键词： Agile Agile Development issue tracking system Version Control system Requirements Knowledge

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Identifying self-admitted technical debt in issue tracking systems using machine learning

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EMPIRICAL SOFTWARE ENGINEERING 2022年第6期27卷 1-37页

作者： Li, Yikun Soliman, Mohamed Avgeriou, Paris Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Groningen Netherlands

Technical debt is a metaphor indicating sub-optimal solutions implemented for short-term benefits by sacrificing the long-term maintainability and evolvability of software. A special type of technical debt is explicitly admitted by software engineers (e.g. using a TODO comment);this is called Self-Admitted Technical Debt or SATD. Most work on automatically identifying SATD focuses on source code comments. In addition to source code comments, issue tracking systems have shown to be another rich source of SATD, but there are no approaches specifically for automatically identifying SATD in issues. In this paper, we first create a training dataset by collecting and manually analyzing 4,200 issues (that break down to 23,180 sections of issues) from seven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase, Impala, and Thrift) using two popular issue tracking systems (i.e., Jira and Google Monorail). We then propose and optimize an approach for automatically identifying SATD in issue tracking systems using machine learning. Our findings indicate that: 1) our approach outperforms baseline approaches by a wide margin with regard to the F1-score;2) transferring knowledge from suitable datasets can improve the predictive performance of our approach;3) extracted SATD keywords are intuitive and potentially indicating types and indicators of SATD;4) projects using different issue tracking systems have less common SATD keywords compared to projects using the same issue tracking system;5) a small amount of training data is needed to achieve good accuracy.

关键词： Self-admitted technical debt Technical debt identification issue tracking system Deep learning Transfer learning

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Managing Assurance Information: A Solution Based on issue tracking systems 20

Managing Assurance Information: A Solution Based on Issue Tr...

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34th Brazilian Symposium on Software Engineering (SBES)

作者： Almendra, Camilo Silva, Carla Univ Fed Ceara Campus Quixada Quixada Ceara Brazil Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)9781450387538

Context. Development of safety assurance cases (SACs) is a trending need for certification of safety-critical systems in regulated domains. Their development encompasses collecting, linking, and structuring diverse project information, such as requirements, hazards, and components. A key building block of SACs is the explanation behind requirements and design decisions, the so-called assurance rationale, which provides compelling argumentation on the system suitability for use. Late development of SACs leads to late discovery of hazards, risks, requirement and design issues that affect the system safety. Question/ problem. Current practice is to manage the assurance rationale in the SAC tools or artefacts, even though it is closely related to project and traceability information. This separation inhibits the shared understanding between safety and software professionals and may cause loss of rationale that results in a poor quality of certification documentation. It also hinders the automated SAC construction and assessment directly from project management tools. Principal ideas/results. This paper presents an approach to support the management of assurance rationale integrated with project information. Our approach defines an assurance rationale model that binds requirements, safety analysis information and assurance case elements. We show the use of the model applied to an issue tracking system. Contribution. We propose a new perspective on assurance cases management by bringing their building blocks into project management tools. Such integration could help bridge Safety and Software Engineering activities and avoid loss of rationale through the system life cycle. Also, it fosters the development of new approaches for SAC generation and assessment.

关键词： Safety assurance case Assurance rationale issue tracking system

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Investigation of IR based Topic Models on issue tracking systems to Infer Software-Specific Semantic Related Term Pairs 10

Investigation of IR based Topic Models on Issue Tracking Sys...

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10th International Conference on Contemporary Computing (IC3)

作者： Correa, Denzil Lal, Sangeeta Sureka, Ashish IIIT D Delhi India Jaypee Inst Informat Technol Noida Uttar Pradesh India ABB Corp Res Bangalore Karnataka India

ISBN: (纸本)9781538630778

Software maintenance is a core component of any software development life-cycle. Contemporary software systems contain voluminous and complex information stored in software repositories. Software maintenance professionals spend significant amount of time in search and exploration of these repositories for common maintenance tasks like bug fixing, feature enhancements, code refactoring and reengineering. Therefore, tools and methods to facilitate search in software repositories can aid software maintenance professionals to have faster access to required information and increase productivity. A domain-specific lexical resource is an important tool to bridge the semantic gap existing between the information need and search query. In this work, we investigate the use of information retrieval (IR) based topic models (like LSI and LDA) to infer semantically related terms for a software context specific lexical resource. We perform our experiments on Google Chromium a widely popular open-source browser - issue tracker system which contains 134,000+ bug reports. We divide our study into two parts -(1) In the first part, we apply our IR models on free form natural language textual data present in defect tracking systems. We perform qualitative analysis on the obtained output and uncover semantically related terms in the Google Chromium software context. We observe that we are able to infer semantically similar term pairs in four different contexts of English language, Software, Google Chromium and Code details. (2) In second part of this study, we utilize the semantically inferred terms obtained from the output of IR models to facilitate the software maintenance task of duplicate bug report detection. Our results demonstrate that the use of IR based topic models on defect tracking systems to automatically infer semantically related terms can help build a software domain-specific lexical resource and reduce the vocabulary gap.

关键词： Automated Software Engineering issue tracking system Latent Dirichlet Allocation (LDA) Latent Semantic Indexing (LSI) Mining Software Repositories Software Maintenance

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Maestro: A Deep Learning Based Tool to Find and Explore Architectural Design Decisions in issue tracking systems 17th

Maestro: A Deep Learning Based Tool to Find and Explore Arch...

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17th European Conference on Software Architecture (ECSA)

作者： Maarleveld, Jesse Dekker, Arjan Druyts, Sarah Soliman, Mohamed Univ Groningen RUG Groningen Netherlands Paderborn Univ Paderborn Germany

ISBN: (纸本)9783031663253;9783031663260

Software engineers commonly re-use architectural design decisions (ADDs) from their previous experience. However, in practice, software engineers still depend on adhoc mechanisms to re-use ADDs. Recent studies show that software engineers discuss ADDs in issue tracking system, which could be useful for software engineers to make new ADDs. Nevertheless, it is rather challenging to find ADDs among the big amount of issues in issue trackers. Therefore, we introduce Maestro, an open source tool for finding, annotating, and exploring ADDs in issue tracking systems. The tool allows researchers and practitioners to find and analyze issues containing ADDs in issue trackers. Maestro provides annotation mechanisms, deep learning components, keywords-based search engine and a user-interface that can be easily used by researchers and practitioners to find and analyze ADDs in issue trackers.

关键词： Architectural design decisions issue tracking system

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Bug report priority prediction using social and technical features

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2023年第6期36卷

作者： Huang, Zijie Shao, Zhiqing Fan, Guisheng Yu, Huiqun Yang, Kang Zhou, Ziyi East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China

Software stakeholders report bugs in issue tracking system (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time-insensitive approaches, and they were producing suboptimal results for practical usage. While they ignored the social aspects of ITS, the technical aspects were also limited in surface features of bug reports. To better model the bug report, we extract their topic and most similar code structures. Since ITS bridges users and developers as the main contributors, we also integrate their experience, sentiment, and socio-technical features to construct a new dataset. Then, we perform two-classed and multiclassed bug priority prediction based on the dataset. We also introduce adversarial training using generated training data with random word swap and random word deletion. We validate our model in within-project, cross-project, and time-wise scenarios, and it outperforms the two baselines by up to 15% in area under curve-receiver operating characteristics (AUC-ROC) and 19% in Matthews correlation coefficient (MCC). We reveal involving contributor (i.e., assignee and reporter) features such as sentiment that could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in social and technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest that researchers should consider both social and technical aspects of ITS in bug report priority prediction and introduce adversarial training to boost model performance. Manual assignment of bug report priority could be time-consuming and

关键词： bug report priority contributor sentiment empirical software engineering issue tracking system socio-technical analysis

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Towards prioritizing user-related issue reports of mobile applications

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EMPIRICAL SOFTWARE ENGINEERING 2019年第4期24卷 1964-1996页

作者： Noei, Ehsan Zhang, Feng Wang, Shaohua Zou, Ying Queens Univ Dept Elect & Comp Engn Kingston ON Canada Queens Univ Sch Comp Kingston ON Canada New Jersey Inst Technol Dept Informat Newark NJ 07102 USA

The competitive market of mobile applications (apps) has driven app developers to pay more attention to addressing the issues of mobile apps. Prior studies have shown that addressing the issues that are reported in user-reviews shares a statistically significant relationship with star-ratings. However, despite the prevalence and importance of user-reviews and issue reports prioritization, no prior research has analyzed the relationship between issue reports prioritization and star-ratings. In this paper, we integrate user-reviews into the process of issue reports prioritization. We propose an approach to map issue reports that are recorded in issue tracking systems to user-reviews. Through an empirical study of 326 open-source Android apps, our approach achieves a precision of 79% in matching user-reviews with issue reports. Moreover, we observe that prioritizing the issue reports that are related to user-reviews shares a significant positive relationship with star-ratings. Furthermore, we use the top apps, in terms of star-ratings, to train a model for prioritizing issue reports. It is a good practice to learn from the top apps as there is no well-established approach for prioritizing issue reports. The results show that mobile apps with a similar prioritization approach to our trained model achieve higher star-ratings.

关键词： Mobile application issue report prioritization User-review issue tracking system

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An Empirical Study on Using Multi-Labels for issues in GitHub

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IEEE ACCESS 2021年 9卷 134984-134997页

作者： Kim, Jindae Lee, Seonah Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul 01811 South Korea Gyeongsang Natl Univ Dept Aerosp & Software Engn Jinju 52828 South Korea Gyeongsang Natl Univ Dept AI Convergence Engn Jinju 52828 South Korea

On GitHub, one of the most successful services for software project hosting, labels have been used to represent various information and decisions about reported issues. However, previous studies on labels were limited to simple statistics or label recommendations for issue types. In this paper, we aim to provide a better understanding of labels and their usage in software development. We particularly focus on using multiple and custom labels on issues. To analyze label usage, we collected software project data and label usage information from GitHub. We then quantitatively investigated the performance of projects with multi-label features and qualitatively investigated the categories of multi-labels, and the usage of multi-labels based on these categories. Our analysis results show that multi-labels are common in the majority of software projects and that projects using multi-label features manage their issues more effectively. In addition, our analysis results reveal different types of information represented by labels, which are related to features, development, and issues. This study finds several facts that can be used for studies on issue management and thus that help develop labeling techniques to mitigate the burden of issue management.

关键词： Software development management Software Labeling Computer bugs Licenses Open source software History issue tracking system issue management issue labels multi-labels github open source software software maintenance

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Self-Admitted Technical Debt in the Embedded systems Industry: An Exploratory Case Study

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 2545-2565页

作者： Li, Yikun Soliman, Mohamed Avgeriou, Paris Somers, Lou Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelligence NL-9712 CP Groningen Netherlands Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands

Technical debt denotes shortcuts taken during software development, mostly for the sake of expedience. When such shortcuts are admitted explicitly by developers (e.g., writing a TODO/Fixme comment), they are termed as Self-Admitted Technical Debt or SATD. There has been a fair amount of work studying SATD management in Open Source projects, but SATD in industry is relatively unexplored. At the same time, there is no work focusing on developers' perspectives towards SATD and its management. To address this, we conducted an exploratory case study in cooperation with an industrial partner to study how they think of SATD and how they manage it. Specifically, we collected data by identifying and characterizing SATD in different sources (issues, source code comments, and commits) and carried out a series of interviews with 12 software practitioners. The results show: 1) the core characteristics of SATD in industrial projects;2) developers' attitudes towards identified SATD and statistics;3) triggers for practitioners to introduce and repay SATD;4) relations between SATD in different sources;5) practices used to manage SATD;6) challenges and tooling ideas for SATD management.

关键词： Source coding Software Codes Industries Interviews Embedded systems Documentation Technical debt self-admitted technical debt mining software repositories source code comment issue tracking system commit empirical study

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