Big data driven genetic improvement for maintenance of legacy software systems
Notice bibliographique
Résumé
Software is vital to modern life, yet much of it is old and suffers from bit-rot. There are not and never will be enough software experts to keep it all up to date by hand. Instead we suggest combining data driven learning with evolutionary search to maintain computer systems. @RE: <1>N. Alshahwan. Industrial experience of genetic improvement in Facebook. In J. Petke, S. H. Tan, W. B. Langdon, and W. Weimer, editors, GI-2019, ICSE workshops proceedings, page 1, Montreal, 28 May 2019. IEEE. Invited Keynote. <2>W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic Programming - An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA, Jan. 1998. <3>N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159--195, Summer 2001. <4>S. Haraldsson, A. Brownlee, and J. R. Woodward. Computers will soon be able to fix themselves - are IT departments for the chop? The Conversation, page 3.29pm BST, Oct. 12 2017. <5>S. O. Haraldsson, J. R. Woodward, A. E. I. Brownlee, and K. Siggeirsdottir. Fixing bugs in your sleep: How genetic improvement became an overnight success. In J. Petke, D. R. White, W. B. Langdon, and W. Weimer, editors, GI-2017, pages 1513--1520, Berlin, 15--19 July 2017. ACM. Best paper. <6>M. Harman and B. F. Jones. Search based software engineering. Information and Software Technology, 43(14):833--839, Dec. 2001. <7>F. Hutter, H. H. Hoos, K. Leyton-Brown, and T. Stuetzle. ParamILS: An automatic algorithm configuration framework. JAIR, 36:267--306, 2009. <8>G. Kendall. Evolutionary computation has been promising self-programming machines for 60 years - so where are they? The Conversation, page 8.54am BST, Mar. 27 2018. <9>J. R. Koza. Genetic Programming: On the Programming of Computers by Natural Selection. MIT press, 1992. <10>W. B. Langdon. Genetic improvement of programs. In R. Matousek, editor, 18th International Con- ference on Soft Computing, MENDEL 2012, Brno, Czech Republic, 27--29 June 2012. Brno University of Technology. Invited keynote. <11>W. B. Langdon and M. Harman. Optimising existing software with genetic programming. IEEE Transactions on Evolutionary Computation, 19(1):118--135, Feb. 2015. <12>W. B. Langdon, B. Y. H. Lam, J. Petke, and M. Harman. Improving CUDA DNA analysis soft- ware with genetic programming. In S. Silva, A. I. Esparcia-Alcazar, M. Lopez-Ibanez, S. Mostaghim, J. Timmis, C. Zarges, L. Correia, T. Soule, M. Giacobini, R. Urbanowicz, Y. Akimoto, T. Glasmach- ers, F. Fernandez de Vega, A. Hoover, P. Larranaga, M. Soto, C. Cotta, F. B. Pereira, J. Handl, J. Koutnik, A. Gaspar-Cunha, H. Trautmann, J.-B. Mouret, S. Risi, E. Costa, O. Schuetze, K. Kraw- iec, A. Moraglio, J. F. Miller, P. Widera, S. Cagnoni, J. Merelo, E. Hart, L. Trujillo, M. Kessentini, G. Ochoa, F. Chicano, and C. Doerr, editors, GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pages 1063--1070, Madrid, 11--15 July 2015. ACM. <13>W. B. Langdon and R. Lorenz. Improving SSE parallel code with grow and graft genetic programming. In J. Petke, D. R. White, W. B. Langdon, and W. Weimer, editors, GI-2017, pages 1537--1538, Berlin, 15--19 July 2017. ACM. <14>W. B. Langdon and J. Petke. Evolving better software parameters. In T. E. Colanzi and P. McMinn, editors, SSBSE 2018 Hot off the Press Track, volume 11036 of LNCS, pages 363--369, Montpellier, France, 8--9 Sept. 2018. Springer. <15>W. B. Langdon, J. Petke, and R. Lorenz. Evolving better RNAfold structure prediction. In M. Castelli, L. Sekanina, and M. Zhang, editors, EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming, volume 10781 of LNCS, pages 220--236, Parma, Italy, 4--6 Apr. 2018. Springer Verlag. <16>C. Le Goues, M. Pradel, and A. Roychoudhury. Automated program repair. Communications of the ACM. To appear. <17>M. Orlov. Evolving software building blocks with FINCH. In J. Petke, D. R. White, W. B. Langdon, and W. Weimer, editors, GI-2017, pages 1539--1540, Berlin, 15--19 July 2017. ACM. <18>L. Perez Caceres, M. Lopez-Ibanez, H. Hoos, and T. Stuetzle. An experimental study of adaptive capping in irace. In R. Battiti, D. E. Kvasov, and Y. D. Sergeyev, editors, Learning and Intelligent Optimization - 11th International Conference, LION 11, Nizhny Novgorod, Russia, June 19--21, 2017, Revised Selected Papers, volume 10556 of Lecture Notes in Computer Science, pages 235--250. Springer, 2017. <19>J. Petke. Constraints: The future of combinatorial interaction testing. In 2015 IEEE/ACM 8th International Workshop on Search-Based Software Testing, pages 17--18, Florence, May 2015. <20>J. Petke, S. O. Haraldsson, M. Harman, W. B. Langdon, D. R. White, and J. R. Woodward. Genetic improvement of software: a comprehensive survey. IEEE Transactions on Evolutionary Computation, 22(3):415--432, June 2018. <21>J. Petke, M. Harman, W. B. Langdon, and W. Weimer. Specialising software for different downstream applications using genetic improvement and code transplantation. IEEE Transactions on Software Engineering, 44(6):574--594, June 2018. <22>R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With contri- butions by J. R. Koza). <23>E. Schulte, S. Forrest, and W. Weimer. Automated program repair through the evolution of assembly code. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering, pages 313--316, Antwerp, 20--24 Sept. 2010. ACM. <24>E. Schulte, W. Weimer, and S. Forrest. Repairing COTS router firmware without access to source code or test suites: A case study in evolutionary software repair. In W. B. Langdon, J. Petke, and D. R. White, editors, Genetic Improvement 2015 Workshop, pages 847--854, Madrid, 11--15 July 2015. ACM. Best Paper. <25>J. R. Woodward, J. Petke, and W. Langdon. How computers are learning to make human software work more efficiently. The Conversation, page 10.08am BST, June 25 2015. <26>K. Yeboah-Antwi and B. Baudry. Online genetic improvement on the java virtual machine with ECSELR. Genetic Programming and Evolvable Machines, 18(1):83--109, Mar. 2017.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».