Efficient Identification of Linchpin Vertices in Dependence Clusters

Abstract

Several authors have found evidence of large dependence clusters in the source code of a diverse range of systems, domains, and programming languages. This raises the question of how we might efficiently locate the fragments of code that give rise to large dependence clusters. We introduce an algorithm for the identification of linchpin vertices, which hold together large dependence clusters, and prove correctness properties for the algorithm's primary innovations. We also report the results of an empirical study concerning the reduction in analysis time that our algorithm yields over its predecessor using a collection of 38 programs containing almost half a million lines of code. Our empirical findings indicate improvements of almost two orders of magnitude, making it possible to process larger programs for which it would have previously been impractical.

Empirical investigation of SEA-based dependence cluster properties

“…First, it has to have a large SEA set (this is similar to one of the findings by Binkley et al [8]). But merely the size of the SEA set is not enough: we measured very low correlation (in the range 0-0.15) between the SEA size and clusterization.…”

“…The naïve linchpin identification algorithm -a brute-force method trying all possible solutions one by one -is not scalable. Hence, previous research that employed fine grained analysis could deal with programs of up to 20 kLOC [4] or 66 kLOC using the advanced method [8]. In a similar fashion, our SEA-based analysis makes it possible to investigate programs with sizes of a magnitude larger thanks to the higher level granularity and a simpler, albeit less precise, analysis method.…”

“…Virtually, the only existing approach to identify linchpins is based on a brute-force method that tries all possibilities. Binkley et al determined a set of conditions which, if met, will exclude certain program elements from being potential linchpins [8], and this way search for linchpins can be significantly optimized. We employ heuristic methods to identify linchpins, and we are not aware of any previous work that used a similar approach.…”

“…As apparently dependence clusters cannot be easily avoided in the majority of cases, research should be focused on understanding the causes for the formation of clusters, and the possibilities for their removal or reduction. Previous work revealed that in many cases a highly focused part of the software can be deemed responsible for the formation of dependence clusters [4,5,8]. Namely, program elements called linchpins are seen as central in terms of dependence relations, and are often holding together the whole program.…”