Project description
A novel methodology for automatic bug fixing
In recent years, the field of software engineering has generated a lot of interest and gained traction thanks to its ability to be incorporated for use by almost any other field. Despite its popularity, however, software engineering has faced major difficulties since its beginning. For instance, bugs can still pop up, with unexpected results for the software. This is why automatic bug fixing has always been a subject of much interest for the software engineering community. However, attempts to provide a solution have encountered problems concerning the methodology used. The EU-funded NATURAL project aims to develop a software contributor that is able to synthesise code using a natural language description of a problem the user faces. The new solution is set to radically transform software engineering.
Objective
Automatic bug fixing, i.e. the idea of having programs that fix other programs, is a long-standing dream that is increasingly embraced by the software engineering community. Indeed, despite the significant effort that humans put into reviewing code and running software test campaigns, programming mistakes slip by, with severe consequences. Fixing those mistakes automatically has recently been the focus of a number of potentially promising techniques. Proposed approaches are however recurrently criticized as being shallow (i.e. they mostly address unit test failures, which are often neither hard nor important problems).
Initial successes in automatic bug fixing are based on scenarios such as the following: when a bug is localized, patches are generated repetitively and automatically, through trial and error, until a valid patch is produced. The produced patch could then be later revised by developers. While the reported achievements are certainly worthwhile, they do not address what we believe is a more comprehensive challenge of software engineering: to systematically fix features of a software system based on end-user requirements.
The ambition of NATURAL is to develop a methodology for yielding an intelligent agent that is capable of receiving a natural language description of a problem that a user faces with a software feature, and then synthesizing code to address this problem so that it meets the user's expectations. Such a repair bot would be a trustworthy software contributor that is 1) first, targeting real bugs in production via exploiting bug reports, which remain largely under-explored, 2) second, aligning with the conversational needs of collaborative work via generating explanations for patch suggestions, 3) third, shifting the repair paradigm towards the design of self-improving systems via yielding novel algorithms that iteratively integrate feedback from humans. Ultimately, NATURAL will be transformative in the practice of software engineering.
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Funding Scheme
ERC-STG - Starting GrantHost institution
4365 ESCH-SUR-ALZETTE
Luxembourg