Rewriting Logic Techniques for Program Analysis and Optimization

Resumen

This thesis proposes a dynamic analysis methodology for improving the diagnosis of erroneous Maude programs. The key idea is to combine runtime assertion checking and dynamic trace slicing for automatically catching errors at runtime while reducing the size and complexity of the erroneous traces to be analyzed (i.e., those leading to states that fail to satisfy the assertions). In the event of an assertion violation, the slicing criterion is automatically inferred, which facilitates the user to rapidly pinpoint the source of the error. First, a technique is formalized that aims at automatically detecting anomalous deviations of the intended program behavior (error symptoms) by using assertions that are checked at runtime. This technique supports two types of user-defined assertions: functional assertions (which constrain deterministic function calls) and system assertions (which specify system state invariants). The proposed dynamic checking is provably sound in the sense that all errors flagged definitely signal a violation of the specifications. Then, upon eventual assertion violations, accurate trace slices (i.e., simplified yet precise execution traces) are generated automatically, which help identify the cause of the error. Moreover, the technique also suggests a possible repair for the rules involved in the generation of the erroneous states. The proposed methodology is based on (i) a logical notation for specifying assertions that are imposed on execution runs; (ii) a runtime checking technique that dynamically tests the assertions; and (iii) a mechanism based on (equational) least general generalization that automatically derives accurate criteria for slicing from falsified assertions. Finally, an implementation of the proposed technique is presented in the assertion-based, dynamic analyzer ABETS, which shows how the forward and backward tracking of asserted program properties leads to a thorough trace analysis algorithm that can be used for program diagnosis and debugging.