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Continuous build outcome prediction: an experimental evaluation and acceptance modelling.

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Title: Continuous build outcome prediction: an experimental evaluation and acceptance modelling.
Authors: Kawalerowicz, Marcin1,2 (AUTHOR) marcin@kawalerowicz.net, Madeyski, Lech3 (AUTHOR)
Source: Applied Intelligence. Apr2023, Vol. 53 Issue 8, p8673-8692. 20p.
Subjects: Software measurement, Technology Acceptance Model, Source code, Machine learning, Historic buildings, Forecasting
Abstract: Continuous Build Outcome Prediction (CBOP) is a lightweight implementation of Continuous Defect Prediction (CDP). CBOP combines: 1) results of continuous integration (CI) and 2) the data mined from the version control system with 3) machine learning (ML) to form a practice that evolved from software defect prediction (SDP) where a failing build is treated as a defect to fight against. Here, we explain the CBOP idea, where we use historical build results together with metrics derived from a software repository to create a model that classifies changes the developer is introducing to the source code during her work in a just-in-time manner. To evaluate the CBOP idea, we perform a small-n repeated measure with two conditions and replicate experiment in a real-life, business-driven software project. In this preliminary evaluation of CBOP, we study whether the practice will reduce the Failed Build Ratio (FBR) - the ratio of failing build results to all other build results. We calculate effect size and p-value of change in FBR while using the CBOP practice, provide an analysis of our model, and perform and report the results of a Technology Acceptance Model (TAM)-inspired survey that we conducted among experiment participants and industry specialists to assess the acceptance of CBOP and the tool. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Continuous Build Outcome Prediction (CBOP) is a lightweight implementation of Continuous Defect Prediction (CDP). CBOP combines: 1) results of continuous integration (CI) and 2) the data mined from the version control system with 3) machine learning (ML) to form a practice that evolved from software defect prediction (SDP) where a failing build is treated as a defect to fight against. Here, we explain the CBOP idea, where we use historical build results together with metrics derived from a software repository to create a model that classifies changes the developer is introducing to the source code during her work in a just-in-time manner. To evaluate the CBOP idea, we perform a small-n repeated measure with two conditions and replicate experiment in a real-life, business-driven software project. In this preliminary evaluation of CBOP, we study whether the practice will reduce the Failed Build Ratio (FBR) - the ratio of failing build results to all other build results. We calculate effect size and p-value of change in FBR while using the CBOP practice, provide an analysis of our model, and perform and report the results of a Technology Acceptance Model (TAM)-inspired survey that we conducted among experiment participants and industry specialists to assess the acceptance of CBOP and the tool. [ABSTRACT FROM AUTHOR]
ISSN:0924669X
DOI:10.1007/s10489-023-04523-6