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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]
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Continuous build outcome prediction: an experimental evaluation and acceptance modelling.
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  Data: <searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Acceptance+Model%22">Technology Acceptance Model</searchLink><br /><searchLink fieldCode="DE" term="%22Source+code%22">Source code</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Historic+buildings%22">Historic buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink>
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  Data: 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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  Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Source code
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      – SubjectFull: Historic buildings
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      – SubjectFull: Forecasting
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              Text: Apr2023
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