TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R
Number of Authors: 1
2016 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 28, 427-459 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
John Wiley & Sons Ltd , 2016, 15. Vol. 28, 427-459 p.
software engineering tools, parameter tuning, experiment framework, empirical software engineering, change impact analysis
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-24548DOI: 10.1002/smr.1784OAI: oai:DiVA.org:ri-24548DiVA: diva2:1043632