MOTSD: A multi-objective test selection tool using test suite diagnosability

Year	Authors	Publisher	DOI
2019	Correia, Daniel Abreu, Rui Santos, Pedro Nadkarni, Joo	ACM	10.1145/3338906.3341187

Year

Authors

Publisher

DOI

2019

Correia, Daniel Abreu, Rui Santos, Pedro Nadkarni, Joo

ACM

10.1145/3338906.3341187

Techniques	Applicability
TCP TCS	Industry Motivation Industry Evaluation Industry Author Put into Practice

Techniques

Applicability

TCP TCS

Industry Motivation Industry Evaluation Industry Author Put into Practice

Experiment subject(s)	Industrial Partner	Programming Language
OutSystems codebase in C# (over 8500 TCs) Industrial proprietary, large scale	OutSystems (Portugal)	C#

Experiment subject(s)

Industrial Partner

Programming Language

OutSystems codebase in C# (over 8500 TCs) Industrial proprietary, large scale

OutSystems (Portugal)

Effectiveness Metrics	Efficiency Metrics	Other Metrics
		Diagnosability

Effectiveness Metrics

Efficiency Metrics

Other Metrics

Diagnosability

Information Approach	Algorithm Approach	Open Challenges
	Search-based	use data other than coverage (e.g. dependency graph); questions regarding evaluation; common benchmark for this type of tool.

Information Approach

Algorithm Approach

Open Challenges

Search-based

use data other than coverage (e.g. dependency graph); questions regarding evaluation; common benchmark for this type of tool.

Supplementary Material
http://github.com/danielcorreia96/MOTSD

Supplementary Material

http://github.com/danielcorreia96/MOTSD

Abstract

Performing regression testing on large software systems becomes unfeasible as it takes too long to run all the test cases every time a change is made. The main motivation of this work was to provide a faster and earlier feedback loop to the developers at OutSystems when a change is made. The developed tool, MOTSD, implements a multi-objective test selection approach in a C# code base using a test suite diagnosability metric and historical metrics as objectives and it is powered by a particle swarm optimization algorithm. We present implementation challenges, current experimental results and limitations of the tool when applied in an industrial context. Screencast demo link: \textlessa\textgreaterhttps://www.youtube.com/watch?v=CYMfQTUu2BE\textless/a\textgreater © 2019 ACM.