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Context-Sensitive Code Completion: Improving Predictions with Genetic Algorithms
KTH, School of Information and Communication Technology (ICT).
2016 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Within the area of context-sensitive code completion there is a need for accurate predictive models in order to provide useful code completion predictions. The traditional method for optimizing the performance of code completion systems is to empirically evaluate the effect of each system parameter individually and fine-tune the parameters.

This thesis presents a genetic algorithm that can optimize the system parameters with a degree-of-freedom equal to the number of parameters to optimize. The study evaluates the effect of the optimized parameters on the prediction quality of the studied code completion system. Previous evaluation of the reference code completion system is also extended to include model size and inference speed.

The results of the study shows that the genetic algorithm is able to improve the prediction quality of the studied code completion system. Compared with the reference system, the enhanced system is able to recognize 1 in 10 additional previously unseen code patterns. This increase in prediction quality does not significantly impact the system performance, as the inference speed remains less than 1 ms for both systems.

Abstract [sv]

Inom området kontextkänslig kodkomplettering finns det ett behov av precisa förutsägande modeller för att kunna föreslå användbara kodkompletteringar. Den traditionella metoden för att optimera prestanda hos kodkompletteringssystem är att empiriskt utvärdera effekten av varje systemparameter individuellt och finjustera parametrarna.

Det här arbetet presenterar en genetisk algoritm som kan optimera systemparametrarna med en frihetsgrad som är lika stor som antalet parametrar att optimera. Studien utvärderar effekten av de optimerade parametrarna på det studerade kodkompletteringssystemets pre- diktiva kvalitet. Tidigare utvärdering av referenssystemet utökades genom att även inkludera modellstorlek och slutledningstid.

Resultaten av studien visar att den genetiska algoritmen kan förbättra den prediktiva kvali- teten för det studerade kodkompletteringssystemet. Jämfört med referenssystemet så lyckas det förbättrade systemet korrekt känna igen 1 av 10 ytterligare kodmönster som tidigare varit osedda. Förbättringen av prediktiv kvalietet har inte en signifikant inverkan på systemet, då slutledningstiden förblir mindre än 1 ms för båda systemen.

Place, publisher, year, edition, pages
2016. , 46 p.
Series
TRITA-ICT-EX, 2016:142
Keyword [en]
Context-sensitive code completion, Genetic algorithms, k-fold cross validation
Keyword [sv]
Kontextkänslig kodkomplettering, Genetiska algoritmer, k-delad korsvalidering
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-205334OAI: oai:DiVA.org:kth-205334DiVA: diva2:1088591
Subject / course
Computer Technology, Program- and System Development
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2017-04-13 Created: 2017-04-13 Last updated: 2017-04-27Bibliographically approved

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Citation style
  • apa
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