Repairing Programs with Semantic Code Search

Automated program repair can potentially reduce debugging costs and improve software quality but recent studies have drawn attention to shortcomings in the quality of automatically generated repairs. We propose a new kind of repair that uses the large body of existing open-source code to find potential fixes. The key challenges lie in efficiently finding code semantically similar (but not identical) to defective code and then appropriately integrating that code into a buggy program. We present SearchRepair, a repair technique that addresses these challenges by (1) encoding a large database of human-written code fragments as SMT constraints on input-output behavior, (2) localizing a given defect to likely buggy program fragments and deriving the desired input-output behavior for code to replace those fragments, (3) using state-of-the-art constraint solvers to search the database for fragments that satisfy that desired behavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program test suites. We find that SearchRepair repairs 150 (19%) of 778 benchmark C defects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by the four techniques by measuring how many independent, not-used-during-repair tests they pass, and find that SearchRepair-repaired programs pass 97.3% of the tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repaired programs pass 68.7%, 72.1%, and 64.2% of the tests, respectively. We conclude that SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects