We present Preambl, an approach that applies automatically generated invariants to regression testing and bug localization. Our invariant generation methodology is Precis, an automatic and scalable engine that uses program predicates to guide clustering of dynamically obtained path information. In this paper, we apply it for regression testing and for capturing program predicates information to guide statistical analysis based bug localization. We present a technique to localize bugs in paths of variable lengths. We are able to map the localized post-deployment bugs on a path to pre-release invariants generated along that path. Our experimental results demonstrate the efficacy of the use of PRECIS for regression testing, as well as the ability of Preambl to zone in on relevant segments of program paths.

We propose PRECIS, a methodology for automatically generating invariants at function and loop boundaries through program path guided clustering. We instrument function inputs and outputs together with predicates for branch conditions and record their values during each execution. Program runs that share the same path are grouped together based on predicate words. For each group with sufficient data we use linear regression to express the output as a function of the inputs. Groups with insufficient data are examined as candidates for clustering with neighboring groups. Candidates that share the same output function are merged into a cluster. For each cluster, we write an invariant that summarizes the behavior of the corresponding set of paths. We evaluate our technique using Siemens benchmarks. When compared to Daikon, we find that our method has significant advantages.