Discovering Neglected Conditions in Software by Mining Dependence Graphs

Abstract—Neglected,conditions are an important but difficult-to-find class of software defects. This paper presents,a novel approach for revealing neglected,conditions that integrates static program,analysis and advanced,data mining techniques,to discover implicit conditional rules in a code base,and to discover rule violations that indicate neglected,conditions. The approach,requires the user to indicate minimal constraints on the context of the rules to be sought, rather than specific rule templates. To permit this generality, rules are modeled as graph minors of enhanced procedure dependence graphs (EPDGs), in which control and data dependence edges are augmented,by edges,representing,shared,data dependences.,A heuristic maximal,frequent subgraph,mining algorithm is used,to extract candidate,rules from EPDGs and a heuristic graph matching,algorithm is used,to identify rule violations. We also report the results of an empirical study in which the approach was applied to four open source projects (openssl, make, procmail, amaya). These results indicate that the approach,is effective and reasonably,efficient. Index Terms—Automatic defect detection, mining software repositories, program dependence graphs, frequent item set mining,