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Search-based automatic program repair has shown promise in reducing the cost of defects in real-world software. However, to date, such techniques have typically been most successful when constructing short or single-edit repairs. This is true even when techniques make use of heuristic search strategies, like genetic programming, that in principle support the construction of patches of arbitrary length. One key reason is that the fitness function traditionally depends entirely on test cases, which are poor at identifying partially correct solutions and lead to a fitness landscape with many plateaus. We propose a novel fitness function that optimizes for both functionality and semantic diversity, characterized using learned invariants over intermediate behavior. Our early results show that this new approach improves semantic diversity and fitness granularity, but does not statistically significantly improve repair performance.
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Learning Fitness Functions for Machine Programming
The problem of automatic software generation has been referred to as machine programming. In this work, we propose a framework based on genetic algorithms to help make progress in this domain. Although genetic algorithms (GAs) have been successfully used for many problems, one criticism is that hand-crafting GAs fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Our framework presents a novel approach to learn the fitness function using neural networks to predict values of ideal fitness functions.We also augment the evolutionary process with a minimally intrusive search heuristic. This heuristic improves the framework’s ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach with several state-of-the-art program synthesis methods and demonstrate that it finds more correct programs with fewer candidate program generations.
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- Award ID(s):
- 1931078
- PAR ID:
- 10320104
- Date Published:
- Journal Name:
- Machine Learning and Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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