Data stream mining for predicting software build outcomes using source code metrics
| aut.relation.endpage | 198 | |
| aut.relation.issue | 2 | |
| aut.relation.startpage | 183 | |
| aut.relation.volume | 56 | |
| aut.researcher | Connor, Andrew Miles | |
| dc.contributor.author | Finlay, J | |
| dc.contributor.author | Pears, R | |
| dc.contributor.author | Connor, AM | |
| dc.date.accessioned | 2014-04-11T09:53:21Z | |
| dc.date.available | 2014-04-11T09:53:21Z | |
| dc.date.copyright | 2014-02-01 | |
| dc.date.issued | 2014-02-01 | |
| dc.description.abstract | Context: Software development projects involve the use of a wide range of tools to produce a software artifact. Software repositories such as source control systems have become a focus for emergent research because they are a source of rich information regarding software development projects. The mining of such repositories is becoming increasingly common with a view to gaining a deeper understanding of the development process. Objective: This paper explores the concepts of representing a software development project as a process that results in the creation of a data stream. It also describes the extraction of metrics from the Jazz repository and the application of data stream mining techniques to identify useful metrics for predicting build success or failure. Method: This research is a systematic study using the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift by applying the Massive Online Analysis (MOA) tool. Results: The results indicate that only a relatively small number of the available measures considered have any significance for predicting the outcome of a build over time. These significant measures are identified and the implication of the results discussed, particularly the relative difficulty of being able to predict failed builds. The Hoeffding Tree approach is shown to produce a more stable and robust model than traditional data mining approaches. Conclusion: Overall prediction accuracies of 75% have been achieved through the use of the Hoeffding Tree classification method. Despite this high overall accuracy, there is greater difficulty in predicting failure than success. The emergence of a stable classification tree is limited by the lack of data but overall the approach shows promise in terms of informing software development activities in order to minimize the chance of failure. | |
| dc.identifier.citation | Information and Software Technology, vol.56(2), pp.183 - 198 | |
| dc.identifier.doi | 10.1016/j.infsof.2013.09.001. | |
| dc.identifier.issn | 0950-5849 | |
| dc.identifier.uri | https://hdl.handle.net/10292/7086 | |
| dc.publisher | Elsevier | |
| dc.relation.uri | http://dx.doi.org/10.1016/j.infsof.2013.09.001 | |
| dc.rights | Copyright © 2014 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version). | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Data stream mining | |
| dc.subject | Concept drift detection | |
| dc.subject | Hoeffding tree | |
| dc.subject | Jazz | |
| dc.subject | Software metrics | |
| dc.subject | Software repositories | |
| dc.title | Data stream mining for predicting software build outcomes using source code metrics | |
| dc.type | Journal Article | |
| pubs.elements-id | 157771 | |
| pubs.organisational-data | /AUT | |
| pubs.organisational-data | /AUT/Design & Creative Technologies | |
| pubs.organisational-data | /AUT/Design & Creative Technologies/Interdisplinary Unit |