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Auto-bug-repair system uses machine learning to fix 10 times as many errors as its predecessors
By Larry Hardesty, MIT News
Adam Conner-Simons, MIT CSAIL
CSAIL researchers have developed a machine-learning system that can comb through repairs to open-source computer programs and learn their general properties, in order to produce new repairs for a different set of programs.
The researchers tested their system on a set of programming errors, culled from real open-source applications, that had been compiled to evaluate automatic bug-repair systems. Where those earlier systems were able to repair one or two of the bugs, the CSAIL system repaired between 15 and 18, depending on whether it settled on the first solution it found or was allowed to run longer.
While an automatic bug-repair tool would be useful in its own right, professor of electrical engineering and computer science Martin Rinard, whose group developed the new system, believes that the work could have broader ramifications.
“One of the most intriguing aspects of this research is that we’ve found that there are indeed universal properties of correct code that you can learn from one set of applications and apply to another set of applications,” Rinard says. “If you can recognize correct code, that has enormous implications across all software engineering. This is just the first application of what we hope will be a brand-new, fabulous technique.”
Fan Long, a graduate student in electrical engineering and computer science at MIT, presented a paper describing the new system at the Symposium on Principles of Programming Languages last week. He and Rinard, his advisor, are co-authors.
Long and Rinard wrote a computer program that evaluated all the possible relationships between these characteristics in successive lines of code. More than 3,500 such relationships constitute their feature set. Their machine-learning algorithm then tried to determine what combination of features most consistently predicted the success of a patch.
“All the features we’re trying to look at are relationships between the patch you insert and the code you are trying to patch,” Long says. “Typically, there will be good connections in the correct patches, corresponding to useful or productive program logic. And there will be bad patterns that mean disconnections in program logic or redundant program logic that are less likely to be successful.”
Long and Rinard’s machine-learning system works in conjunction with this earlier algorithm, ranking proposed modifications according to the probability that they are correct before subjecting them to time-consuming tests.
The researchers tested their system, which they call Prophet, on a set of 69 program errors that had cropped up in eight popular open-source programs. Of those, 19 are amenable to the type of modifications that Long’s algorithm uses; the other 50 have more complicated problems that involve logical inconsistencies across larger swaths of code.
When Long and Rinard configured their system to settle for the first solution that passed the bug-eliciting tests, it was able to correctly repair 15 of the 19 errors; when they allowed it to run for 12 hours per problem, it repaired 18.
Of course, that still leaves the other 50 errors in the test set untouched. In ongoing work, Long is working on a machine-learning system that will look at more coarse-grained manipulation of program values across larger stretches of code, in the hope of producing a bug-repair system that can handle more complex errors.
“A revolutionary aspect of Prophet is how it leverages past successful patches to learn new ones,” says Eran Yahav, an associate professor of computer science at the Technion in Israel. “It relies on the insight that despite differences between software projects, fixes — patches — applied to projects often have commonalities that can be learned from. Using machine learning to learn from ‘big code’ holds the promise to revolutionize many programming tasks — code completion, reverse-engineering, et cetera.”