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Effective Recommendation of Cross-Project Correlated Issues based on Issue Metrics

Published: 05 October 2023 Publication History

Abstract

The calling relationship between projects becomes complicated as the number of open-source projects increases. Different issues across projects can also be related, referred to as cross-project correlated issues (CPCIs), and bring new challenges for developers to fix these issues. When solving these CPCIs, developers have to accurately locate the source code that causes it in the current project and also needs to know the related issues in other projects. However, few studies have proposed specific methods to help developers effectively address these CPCIs, i.e., find related issues for CPCIs.
This paper proposes a novel issue recommendation model for CPCIs. When developers fix a CPCI, they can find its associated issues based on our model. We first extract 26 issue metrics on CPCIs from four aspects: text similarity, cooperative relationship between developers, developers’ familiarity with the project, and developers’ fixing experience. Then, we utilize three classifiers (SVM, Logistic Regression, and Random Forest) to build CPCI recommendation models. To evaluate the model’s performance, we construct three baseline models based on text features and build experiments in the Python scientific computing software ecosystem, which mainly includes seven open-source software libraries. Moreover, we employ three indicators to measure the experimental results, i.e., MAP, MRR, and Recall-rate@k. The CPCI recommendation models built based on issue features have significantly better experimental results than the baseline models in most cases, which indicates that these issue metrics help recommend CPCIs.

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cover image ACM Other conferences
Internetware '23: Proceedings of the 14th Asia-Pacific Symposium on Internetware
August 2023
332 pages
ISBN:9798400708947
DOI:10.1145/3609437
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Published: 05 October 2023

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  1. Cross-project Correlated Issues
  2. Metrics
  3. Recommendation Model

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  • Research-article
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  • National Natural Science Foundation of China

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Internetware 2023

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