A Novel Fitness Function for Automated Program Repair Based on Source Code Checkpoints

Software maintenance, especially bug fixing, is one of the most expensive problems in software practice. Bugs have global impact in terms of cost and time, and they also reflect negatively on a company's brand. GenProg is a method for Automated Program Repair based on an evolutionary approach. It aims to generate bug repairs without human intervention or a need for special instrumentation or source code annotations. Its canonical fitness function evaluates each variant as the weighted sum of the test cases that a modified program passes. However, it evaluates distinct individuals with the same fitness score (plateaus). We propose a fitness function that minimizes these plateaus using dynamic analysis to increase the granularity of the fitness information that can be gleaned from test case execution, increasing the diversity of the population, the number of repairs found (expressiveness), and the efficiency of the search. We evaluate the proposed fitness functions on two standard benchmarks for Automated Program Repair: IntroClass and ManyBugs. We find that our proposed fitness function minimizes plateaus, increases expressiveness, and the efficiency of the search.