Tuning for Software Analytics: is it Really Necessary?

TL;DR

This paper demonstrates that simple, automatic tuning methods significantly improve software defect prediction accuracy, challenging the assumption that extensive tuning is necessary for effective software analytics.

Contribution

It introduces an efficient differential evolution-based tuning approach that requires only tens of attempts to optimize defect predictors, showing substantial performance gains.

Findings

Tuning can dramatically improve defect prediction precision from 0% to 60%.

Simple tuning methods are surprisingly effective and quick.

Standard practices should incorporate tuning optimization for better results.

Abstract

Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well compared to manual methods, and they are easy to use and useful to use. But one of the "black art" of data mining is setting the tunings that control the miner. Objective:We seek simple, automatic, and very effective method for finding those tunings. Method: For each experiment with different data sets (from open source JAVA systems), we ran differential evolution as anoptimizer to explore the tuning space (as a first step) then tested the tunings using hold-out data. Results: Contrary to our prior expectations, we found these tunings were remarkably simple: it only required tens, not thousands,of attempts to obtain very good results. For example, when learning software defect predictors, this method can quickly find tuningsthat alter detection precision from 0% to 60%. Conclusion: Since (1) the improvements are so large, and (2) the tuning is so simple, we need to change standard methods insoftware analytics. At least for defect prediction, it is no longer enough to just run a data miner and present the resultwithoutconducting a tuning optimization study. The implication for other kinds of analytics is now an open and pressing issue