TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R

Borg, Markus (2016) TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R. Journal of Software: Evolution and Process, 28 (6). pp. 427-459. ISSN 2047-7481

PDF (Pre-print) - Submitted Version

Official URL:


Numerous tools automating various aspects of software engineering have been developed, and many of the tools are highly configurable through parameters. Understanding the parameters of advanced tools often requires deep understanding of complex algorithms. Unfortunately, suboptimal parameter settings limit the performance of tools and hinder industrial adaptation, but still few studies address the challenge of tuning software engineering tools. We present TuneR, an experiment framework that supports finding feasible parameter settings using empirical methods. The framework is accompanied by practical guidelines of how to use R to analyze the experimental outcome. As a proof-of-concept, we apply TuneR to tune ImpRec, a recommendation system for change impact analysis in a software system that has evolved for more than two decades. Compared with the output from the default setting, we report a 20.9% improvement in the response variable reflecting recommendation accuracy. Moreover, TuneR reveals insights into the interaction among parameters, as well as nonlinear effects. TuneR is easy to use, thus the framework has potential to support tuning of software engineering tools in both academia and industry.

Item Type:Article
Uncontrolled Keywords:software engineering tools, parameter tuning, experiment framework, empirical software engineering, change impact analysis
ID Code:6019
Deposited By:Markus Borg
Deposited On:04 May 2016 11:38
Last Modified:09 Aug 2016 10:39

Repository Staff Only: item control page