Predicting Test Case Verdicts Using TextualAnalysis of Commited Code Churns
Paper in proceeding, 2019

Background: Continuous Integration (CI) is an agile software development practice that involves producing several clean builds of the software per day. The creation of these builds involve running excessive executions of automated tests, which is hampered by high hardware cost and reduced development velocity. Goal: The goal of our research is to develop a method that reduces the number of executed test cases at each CI cycle.Method: We adopt a design research approach with an infrastructure provider company to develop a method that exploits Ma-chine Learning (ML) to predict test case verdicts for committed sourcecode. We train five different ML models on two data sets and evaluate their performance using two simple retrieval measures: precision and recall. Results: While the results from training the ML models on the first data-set of test executions revealed low performance, the curated data-set for training showed an improvement on performance with respect to precision and recall. Conclusion: Our results indicate that the method is applicable when training the ML model on churns of small sizes

Code Churn


Test Case Selection

Machine Learning


Khaled Al Sabbagh

University of Gothenburg

Miroslaw Staron

University of Gothenburg

Regina Hebig

University of Gothenburg

Wilhelm Meding


CEUR Workshop Proceedings

16130073 (ISSN)

Vol. 2476 138-153 7

International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement
Haarlem, Netherlands,

Subject Categories

Software Engineering

Computer Science

Computer Systems

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