Paper Info
Title
Data stream mining for predicting software build outcomes using source code metrics
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.
Year
DOI
Venue
2014
10.1016/j.infsof.2013.09.001
Information and Software Technology
Keywords
Field
DocType
software repository,software artifact,traditional data mining approach,software development project,source code metrics,development process,hoeffding tree approach,hoeffding tree classification method,data stream mining technique,data stream,software development activity,data mining,software development,sliding window,jazz,source code,source control,data stream mining,concept drift,software metrics,classification tree
Data mining,Data stream mining,Source code,Computer science,Software build,Concept drift,Software,Software metric,Software development,Decision tree learning
Journal
Volume
Issue
ISSN
abs/1604.05503
2
Information & Software Technology, 56(2), 183-198 (2014)
Citations 
PageRank 
References 
11
0.56
39
Authors
3
Name
Order
Citations
PageRank
Jacqui Finlay1232.15
Russel Pears220527.00
Andy Connor37212.71