The Why, When, What and How about Predictive Continuous Integration : A Simulation-Based Investigation

Journal article


Liu, Bohan, Zhang, He, Ma, Weigang, Li, Gongyuan, Li, Shanshan and Shen, Haifeng. (2023). The Why, When, What and How about Predictive Continuous Integration : A Simulation-Based Investigation. IEEE Transactions on Software Engineering. 49(12), pp. 5223-5249. https://doi.org/10.1109/TSE.2023.3330510
AuthorsLiu, Bohan, Zhang, He, Ma, Weigang, Li, Gongyuan, Li, Shanshan and Shen, Haifeng
Abstract

Continuous Integration (CI) enables developers to detect defects early and thus reduce lead time. However, the high frequency and long duration of executing CI have a detrimental effect on this practice. Existing studies have focused on using CI outcome predictors to reduce frequency. Since there is no reported project using predictive CI, it is difficult to evaluate its economic impact. This research aims to investigate predictive CI from a process perspective, including why and when to adopt predictors, what predictors to be used, and how to practice predictive CI in real projects. We innovatively employ Software Process Simulation to simulate a predictive CI process with a Discrete-Event Simulation (DES) model and conduct simulation-based experiments. We develop the Rollback-based Identification of Defective Commits (RIDEC) method to account for the negative effects of false predictions in simulations. Experimental results show that: 1) using predictive CI generally improves the effectiveness of CI, reducing time costs by up to 36.8% and the average waiting time before executing CI by 90.5%; 2) the time-saving varies across projects, with higher commit frequency projects benefiting more; and 3) predictor performance does not strongly correlate with time savings, but the precision of both failed and passed predictions should be paid more attention. Simulation-based evaluation helps identify overlooked aspects in existing research. Predictive CI saves time and resources, but improved prediction performance has limited cost-saving benefits. The primary value of predictive CI lies in providing accurate and quick feedback to developers, aligning with the goal of CI.

KeywordsContinuous integration; machine learning; software process simulation; discrete-event simulation
Year01 Jan 2023
JournalIEEE Transactions on Software Engineering
Journal citation49 (12), pp. 5223-5249
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN0098-5589
Digital Object Identifier (DOI)https://doi.org/10.1109/TSE.2023.3330510
Web address (URL)https://ieeexplore.ieee.org/document/10315109
Open accessPublished as non-open access
Research or scholarlyResearch
Page range5223-5249
Publisher's version
Output statusPublished
Publication dates
PrintDec 2023
Publication process dates
AcceptedNov 2022
Deposited10 Jun 2024
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© Copyright 2023 IEEE. All rights reserved.

Place of publicationUnited States
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