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research-article

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

Authors:

Muhammad Mateen,

Junhao WenAuthors Info & Claims

Frontiers of Computer Science, Volume 16, Issue 6

https://doi.org/10.1007/s11704-021-1013-5

Published: 01 December 2022 Publication History

Abstract

As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people’s daily life, which have the characteristics of frequent updates involving in many code commits to meet new requirements. Just-in-Time (JIT) defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers, which is more suitable to mobile apps. As the within-app defect prediction needs sufficient historical data to label the commit instances, which is inadequate in practice, one alternative method is to use the cross-project model. In this work, we propose a novel method, called KAL, for cross-project JIT defect prediction task in the context of Android mobile apps. More specifically, KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features. Then, the adversarial learning technique is used to extract the common feature embedding for the model building. We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation. The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

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Cited By

Jiang SHe ZChen YZhang MMa L(2024)Mobile Application Online Cross-Project Just-in-Time Software Defect Prediction FrameworkACM Transactions on Software Engineering and Methodology10.1145/366460733:6(1-31)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3664607
Xin QWu HTang JLiu XReiss SXuan J(2024)Detecting, Creating, Repairing, and Understanding Indivisible Multi-Hunk BugsProceedings of the ACM on Software Engineering10.1145/36608281:FSE(2747-2770)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660828
Yang PZhu LHu WKeung JLu LXiang J(2023)The Impact of the bug number on Effort-Aware Defect Prediction: An Empirical StudyProceedings of the 14th Asia-Pacific Symposium on Internetware10.1145/3609437.3609458(67-78)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3609437.3609458
Show More Cited By

Index Terms

Effort-aware cross-project just-in-time defect prediction framework for mobile apps
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
    2. Software verification and validation
  2. Software notations and tools
    1. Software configuration management and version control systems

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 16, Issue 6

Dec 2022

189 pages

ISSN:2095-2228

EISSN:2095-2236

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© Higher Education Press 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2022

Accepted: 25 June 2021

Received: 11 January 2021

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Cited By

Jiang SHe ZChen YZhang MMa L(2024)Mobile Application Online Cross-Project Just-in-Time Software Defect Prediction FrameworkACM Transactions on Software Engineering and Methodology10.1145/366460733:6(1-31)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3664607
Xin QWu HTang JLiu XReiss SXuan J(2024)Detecting, Creating, Repairing, and Understanding Indivisible Multi-Hunk BugsProceedings of the ACM on Software Engineering10.1145/36608281:FSE(2747-2770)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660828
Yang PZhu LHu WKeung JLu LXiang J(2023)The Impact of the bug number on Effort-Aware Defect Prediction: An Empirical StudyProceedings of the 14th Asia-Pacific Symposium on Internetware10.1145/3609437.3609458(67-78)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3609437.3609458
van Dinter RCatal CGiray GTekinerdogan B(2023)Just-in-time defect prediction for mobile applications: using shallow or deep learning?Software Quality Journal10.1007/s11219-023-09629-131:4(1281-1302)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.1007/s11219-023-09629-1

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