Fitness Guided Fault Localization with Coevolutionary Automated Software Correction Case Study ISC Graduate Student: Josh Wilkerson, Computer Science ISC PI: Dr. Daniel Tauritz, Computer Science Co-PIs:Dr. Bruce McMillin, Computer Science & Dr. Thomas Weigert, Computer Science Missouri S&T Natural Computation Laboratory Technical Background: The FGFL System Fault localization: the most time consuming step in software debugging Would greatly benefit from automation FGFL: automated fault localization system exploiting a fitness function for the program Fitness function: type of objective function used to rate the performance of a solution Ensemble of techniques used in FGFL, currently consisting of: Trace Comparison Trend Based Line Suspicion Fitness monitor Results of (active) techniques combined using confidence based voting system Technical Background: Multi-Objective Optimization Simultaneously optimize problem objectives Candidate solutions are given scores for each objective Non-Dominated Sorting Genetic Algorithm (NSGA): algorithm that FGFL multi-objective approach is based on NSGA Details: Solution fitness is proportional to the number of other solutions dominated Solution S1 dominates S2 if S1 performs at least as good as S2 on all objectives and better on at least one objective Non-Dominated Front: set of solutions that are not dominated by any other known solutions Benefits and Motivation Most real world problems consist of multiple objectives  Multi-objective fitness calculation allows problems to remain closer to their natural state in the FGFL system Combining objectives together into a single aggregate fitness function is difficult, as there infinite possibilities  Multi-objective fitness functions avoid having to make tradeoff decisions a priori The dominance relationship between solutions is stronger than comparison employing an aggregate fitness function  Multi-objective comparison is expected to lead to an improved FGFL test case set Separate objective scores provide more insight into a solution’s performance  FGFL enhancements and techniques that specifically exploit multiple objective functions Objective Add support for multi-objective optimization style fitness calculation to the Fitness Guided Fault Localization (FGFL) System Proposed FGFL Enhancement: Objective Sensitivity Testing Through testing, objectives effected by a bug could be identified Identifying objectives sensitive to the bug will allow for more targeted testing  Ideally resulting in more accurate results Removing objectives from consideration would result in a significant search space reduction Current and Future Work Continue development and testing of FGFL enhancements and new techniques exploiting multiple objective functions Investigate enhancements to existing FGFL techniques to exploit multiple objective functions Conduct case study using the Coevolutionary Automated Software Correction (CASC) system  Use FGFL to indicate section of buggy code to be corrected Use both aggregate and multi-objective function  Testing using the Siemens test suite of programs with injected faults Proposed FGFL Technique: Cross Objective Testing Examine execution characteristics of test cases on the non-dominated front with sufficiently unique performance  The closer to the pareto-optimal front, the better Differences in these characteristics can be employed to provide information on the nature and location of the bug Can also be used to enhance existing FGFL techniques Example Fault Localization Result: Warmer Color Indicates Higher Suspicion Example Front Organization