This presentation explores the innovative approach of automatic bug repair through genetic programming, led by researcher Claire Le Goues. It discusses how humans typically fix bugs and the limitations faced by developers. The solution hinges on a genetic programming framework called GenProg, which employs a randomized search to generate program edits aimed at correcting errors while utilizing existing test cases for effective evaluation. With an annual cost of software errors in the U.S. reaching $59.5 billion, automating bug repair processes is essential for improving software reliability and reducing maintenance burdens.
01 / 161Automatic program repair using genetic programming
1 / 17
Télécharger la présentation
Automatic program repair using genetic programming
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation
Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement.Téléchargez la présentation en cliquant sur ce lien.Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.
Input: 1 2 4 3 7 5 6 Legend: Likely faulty. probability Maybe faulty. probability Not faulty. 9 10 8 11 12 http://www.clairelegoues.com
“Everyday, almost 300 bugs appear […] far too many for only the Mozilla programmers to handle.” • – Mozilla Developer, 2005 • Annual cost of software errors in the US: $59.5 billion (0.6% of GDP). • Average time to fix a security-critical error: 28 days. Problem: Buggy Software 10%: Everything Else 90%: Maintenance http://www.clairelegoues.com
The plan • Input: program, test cases encoding required functionality, at least one (failing!) test case encoding the bug. • Approach: use genetic programming to conduct a biased, random search for a set of edits to a program that fixes a given bug. http://www.clairelegoues.com
Genetic programming • Population of variants. • Fitness function evaluates desirability. • Desirable individuals are more likely to be selected for iteration and reproduction. • New variants created via: • Mutation • Crossover http://www.clairelegoues.com
Genetic programming • Population of variants. • Fitness function evaluates desirability. • Desirable individuals are more likely to be selected for iteration and reproduction. • New variants created via: • Mutation • Crossover ABCDEF ABADEF http://www.clairelegoues.com
Genetic programming • Population of variants. • Fitness function evaluates desirability. • Desirable individuals are more likely to be selected for iteration and reproduction. • New variants created via: • Mutation • Crossover ABCDEF ABCWVU ZYXWVU ZYXDEF http://www.clairelegoues.com
Secret Sauces • There are many ways to fix any bug • Upshot: coarse-grained edits. • Not every line of code is equally likely to contribute to the bug. • Upshot: use test cases to refine mutation probabilities. • Programs contain the seeds of bug repair; programmers know what they’re doing. • Upshot: do not invent new code. http://www.clairelegoues.com
Fitness • Compile and run each candidate: • Fitness = weighted count of passed test cases. • Heuristic: passing the bug test is worth more. • If it fails to compile, fitness = 0. • Selection and Crossover: higher fitness variants are (randomly) retained and combined into the next generation. http://www.clairelegoues.com
Mutation: Bird’s Eye View • Search: random (GP) search through population of patches. • Approach: compose small random edits. • Where to change? • How to change it? http://www.cs.virginia.edu/~csl9q
Mutation: Where • Use the test cases to associate bug with code: • Instrument program. • Record which statements are executed on failing vs. passing test cases. • Weight statements accordingly. • Initial weighting heuristic: • High (1.0) if S is only visited on a failed test • Low (0.1) if S is visited on both failed and passed tests • Zero (0.0) if S is not visited on any failed test. http://www.clairelegoues.com
Mutation: How • Three statement-leveledits: • delete X • replace X with Y • insert Y after X. • To mutate a variant: • Choose a random program statement S. • Choose a random edit to apply to S. • Append edit to the variant. • Replace/insert: pick Y from somewhere else in the program. http://www.clairelegoues.com
Mutation: How Coarse! Reduces search space by at least 2—10x. • Three statement-leveledits: • delete X • replace X with Y • insert Y after X. • To mutate a variant: • Choose a random program statement S. • Choose a random edit to apply to S. • Append edit to the variant. • Replace/insert: pick Y from somewhere else in the program. http://www.clairelegoues.com
Mutation: How Coarse! Reduces search space by at least 2—10x. • Three statement-leveledits: • delete X • replace X with Y • insert Y after X. • To mutate a variant: • Choose a random program statement S. • Choose a random edit to apply to S. • Append edit to the variant. • Replace/insert: pick Y from somewhere else in the program. http://www.clairelegoues.com
Mutation: How Coarse! Reduces search space by at least 2—10x. • Three statement-leveledits: • delete X • replace X with Y • insert Y after X. • To mutate a variant: • Choose a random program statement S. • Choose a random edit to apply to S. • Append edit to the variant. • Replace/insert: pick Y from somewhere else in the program. Biased by fault localization. http://www.clairelegoues.com
Mutation: How Coarse! Reduces search space by at least 2—10x. • Three statement-leveledits: • delete X • replace X with Y • insert Y after X. • To mutate a variant: • Choose a random program statement S. • Choose a random edit to apply to S. • Append edit to the variant. • Replace/insert: pick Y from somewhere else in the program. Biased by fault localization. Selection probabilities set heuristically. http://www.clairelegoues.com
Connexion au compte
Connectez-vous pour publier votre contenu sur SlideServe.
