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Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35%) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35%) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair's success. Manually improving fault localization allows SOSRepair to patch 24 (37%) defects, of which 16 (67%) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches. 

Software specifications often use natural language to describe the desired behavior, but such specifications are difficult to verify automatically. We present Swami, an automated technique that extracts test oracles and generates executable tests from structured natural language specifications. Swami focuses on exceptional behavior and boundary conditions that often cause field failures but that developers often fail to manually write tests for. Evaluated on the official JavaScript specification (ECMA-262), 98.4% of the tests Swami generated were precise to the specification. Using Swami to augment developer-written test suites improved coverage and identified 1 previously unknown defect and 15 missing JavaScript features in Rhino, 1 previously unknown defect in Node.js, and 18 semantic ambiguities in the ECMA-262 specification.