Mining Software Metrics from Jazz

TL;DR

This paper extracts software metrics from the Jazz repository and applies data mining to identify key metrics that predict build success or failure, revealing that only a few metrics are significant predictors.

Contribution

It introduces a systematic approach to extract and analyze software metrics from Jazz, demonstrating their predictive power for build outcomes using data mining techniques.

Findings

Few metrics significantly predict build success or failure

Predicting failed builds remains challenging

Identified key metrics relevant for build outcome prediction

Abstract

In this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of an attempt to construct a working instance of the software product. We present results from a systematic study using the J48 classification method. The results indicate that only a relatively small number of the available software metrics that we considered have any significance for predicting the outcome of a build. These significant metrics are discussed and implication of the results discussed, particularly the relative difficulty of being able to predict failed build attempts.