Automatically recommending components for issue reports using deep learning View Full Text


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Article Info

DATE

2021-02-02

AUTHORS

Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Chaiyong Ragkhitwetsagul, Aditya Ghose

ABSTRACT

Today’s software development is typically driven by incremental changes made to software to implement a new functionality, fix a bug, or improve its performance and security. Each change request is often described as an issue. Recent studies suggest that a set of components (e.g., software modules) relevant to the resolution of an issue is one of the most important information provided with the issue that software engineers often rely on. However, assigning an issue to the correct component(s) is challenging, especially for large-scale projects which have up to hundreds of components. In this paper, we propose a predictive model which learns from historical issue reports and recommends the most relevant components for new issues. Our model uses Long Short-Term Memory, a deep learning technique, to automatically learn semantic features representing an issue report, and combines them with the traditional textual similarity features. An extensive evaluation on 142,025 issues from 11 large projects shows that our approach outperforms one common baseline, two state-of-the-art techniques, and six alternative techniques with an improvement of 16.70%–66.31% on average across all projects in predictive performance. More... »

PAGES

14

References to SciGraph publications

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  • 2017-07-24. Automated classification of software issue reports using machine learning techniques: an empirical study in INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING
  • 2014-12-25. Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context in MINING, MODELING, AND RECOMMENDING 'THINGS' IN SOCIAL MEDIA
  • 2013-11-15. Tag recommendation for open source software in FRONTIERS OF COMPUTER SCIENCE
  • 2019-04-12. An Improved Classifier Based on Entropy and Deep Learning for Bug Priority Prediction in INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS
  • 2014-08-03. Automated prediction of bug report priority using multi-factor analysis in EMPIRICAL SOFTWARE ENGINEERING
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  • 2012-10-10. On using machine learning to automatically classify software applications into domain categories in EMPIRICAL SOFTWARE ENGINEERING
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s10664-020-09898-5

    DOI

    http://dx.doi.org/10.1007/s10664-020-09898-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1135072423


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