Paper Info
Title
Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis.
Abstract
Software change impact analysis aims to find artifacts potentially affected by a change. Typical approaches apply language-specific static or dynamic dependence analysis, and are thus restricted to homogeneous systems. This restriction is a major drawback given today's increasingly heterogeneous software. Evolutionary coupling has been proposed as a language-agnostic alternative that mines relations between source-code entities from the system's change history. Unfortunately, existing evolutionary coupling based techniques fall short. For example, using Singular Value Decomposition (SVD) quickly becomes computationally expensive. An efficient alternative applies targeted association rule mining, but the most widely known approach (ROSE) has restricted applicability: experiments on two large industrial systems, and four large open source systems, show that ROSE can only identify dependencies about 25% of the time. To overcome this limitation, we introduce TARMAQ, a new algorithm for mining evolutionary coupling. Empirically evaluated on the same six systems, TARMAQ performs consistently better than ROSE and SVD, is applicable 100% of the time, and runs orders of magnitude faster than SVD. We conclude that the proposed algorithm is a significant step forward towards achieving robust change impact analysis for heterogeneous systems.
Year
DOI
Venue
2016
10.1109/SANER.2016.101
SANER
Keywords
Field
DocType
change recommendations,evolutionary coupling,recommender systems,software change impact analysis,targeted association rule mining
Recommender system,Change impact analysis,Singular value decomposition,Data mining,Computer science,Tracking system,Dependence analysis,Theoretical computer science,Software system,Software,Association rule learning
Conference
Volume
Citations 
PageRank 
1
11
0.51
References 
Authors
24
5
Name
Order
Citations
PageRank
Thomas Rolfsnes1282.84
Stefano Di Alesio2877.57
Razieh Behjati3957.75
Leon Moonen4143272.21
Dave Binkley524913.38