Studying software evolution of large object‐oriented software systems using an ETGM algorithm

Abstract

This paper presents a graph matching approach for the study of Object Oriented software systems' evolution. Class diagrams of different releases are modelled as directed graphs and matched using an ErrorTolerant Graph Matching (ETGM) algorithm. classes and thus likely constitute the backbone of the system. An extensive empirical evaluation of our algorithm using small (Rhino), medium (Azureus and ArgoUML), and large systems (Mozilla and Eclipse) show that our algorithm is fast, scalable and stable. SUMMARYAnalyzing and understanding the evolution of large object-oriented software systems is an important but difficult task in which matching algorithms play a fundamental role. An error-tolerant graph matching (ETGM) algorithm can identify evolving classes that maintain a stable structure of relations (associations, inheritances, and aggregations) with other classes and thus likely constitute the backbone of the system. Therefore, to study the evolution of class diagrams, we first develop a novel ETGM algorithm, which improves the performance of our previous algorithm. Second, we describe the process of building an oracle to validate the results of our approach to solve the class diagram evolution problem. Third, we report for the new algorithm the impact of its parameters on the F-measure summarizing precision (quantifying the exactness of the solution) and recall (quantifying the completeness of the solution). Finally, with tuned parameters, we carry out and report an extensive empirical evaluation of our algorithm using small (Rhino), medium (Azureus and ArgoUML), and large systems (Mozilla and Eclipse). We thus show that this novel algorithm is scalable, stable and has better time performance than its earlier version.

MADMatch: Many-to-Many Approximate Diagram Matching for Design Comparison

“…In software engineering, research literature on diagram matching is mostly focused on source code and class diagrams [1], [2], [12], [18], [28], [30], [31]. Other related problems include the matching of sequence diagrams [22] or labeled transition systems [4].…”

“…The present paper builds on our previous work on approximate graph matching, from theoretical experiments [17] to software engineering applications [18]. In particular, MADMatch is an extension of our previous work on the evolution of large objectoriented (OO) systems [18], which also featured a tabu search for a combinatorial optimization formulation.…”

“…In particular, MADMatch is an extension of our previous work on the evolution of large objectoriented (OO) systems [18], which also featured a tabu search for a combinatorial optimization formulation. There are, however, many key differences and we claim four new contributions.…”

“…Many matching algorithms have been proposed to help developers in identifying structural changes between the designs of subsequent releases of large OO systems [1], [12], [18], [8], [27], [30], [31]. In a typical setting, a design representation, usually a class diagram, is first recovered from the code and then a matching algorithm is applied to pairs of versions to gain insight on the design evolution.…”

“…• dropNodeRatio = 0.7 → for a high tolerance to text dissimilarity • dropArcRatio = 0.7 → for a high tolerance to structure dissimilarity • arcWeight = 0.2 → information brought by one relationship is about 20% of information brought by one node • nameWeight = 0.5 → the entity name counts for half the text similarity between two entities • asymmetry = 1 → the matching direction is not taken into account The above setting is inspired from previous experiments that we conducted on class diagrams [18] in a one-to-one matching context.…”