article

Improving code completion with program history

Authors:

Michele LanzaAuthors Info & Claims

Automated Software Engineering, Volume 17, Issue 2

Pages 181 - 212

https://doi.org/10.1007/s10515-010-0064-x

Published: 01 June 2010 Publication History

Abstract

Code completion is a widely used productivity tool. It takes away the burden of remembering and typing the exact names of methods or classes: As a developer starts typing a name, it provides a progressively refined list of candidates matching the name. However, the candidate list usually comes in alphabetic order, i.e., the environment is only second-guessing the name based on pattern matching, relying on human intervention to pick the correct one. Finding the correct candidate can thus be cumbersome or slower than typing the full name.

We present an approach to improve code completion based on recorded program histories. We define a benchmarking procedure measuring the accuracy of a code completion engine and apply it to several completion algorithms on a dataset consisting of the history of several systems. Further, we use the change history data to improve the results offered by code completion tools. Finally, we propose an alternative interface for completion tools that we released to developers and evaluated.

References

[1]

Arisholm, E., Gallis, H., Dybå, T., Sjøberg, D.I.K.: Evaluating pair programming with respect to system complexity and programmer expertise. IEEE Trans. Softw. Eng. 33(2), 65-86 (2007).

Digital Library

[2]

Bajracharya, S., Ngo, T., Linstead, E., Dou, Y., Rigor, P., Baldi, P., Lopes, C.: Sourcerer: a search engine for open source code supporting structure-based search. In: OOPSLA '06: Companion to the 21st ACM SIGPLAN Symposium on Object-Oriented Programming Systems, Languages, and Applications, pp. 681-682. ACM, New York (2006). http://doi.acm.org/10.1145/1176617.1176671

[3]

Bruch, M., Monperrus, M., Mezini, M.: Learning from examples to improve code completion systems. In: ESEC/FSE'09: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 213-222 (2009).

Digital Library

[4]

Conradi, R., Westfechtel, B.: Version models for software configuration management. ACM Comput. Surv. 30(2), 232-282 (1998).

Digital Library

[5]

Dehnadi, S., Bornat, R.: The camel has two humps (working title) (2006). http://www.cs.mdx.ac.uk/ research/PhDArea/saeed/paper1.pdf

[6]

Fazly, A.: The use of syntax in word completion utilities. Master's thesis, University of Toronto (2002).

[7]

Murphy, G., Kersten, M., Findlater, L.: How are java software developers using the eclipse ide? IEEE Softw. (2006).

[8]

Kersten, M., Murphy, G.C.: Using task context to improve programmer productivity. In: FSE '06: Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 1-11 (2006).

Digital Library

[9]

Little, G., Miller, R.C.: Keyword programming in Java. In: ASE '07: Proceedings of the 22nd International Conference on Automated Software Engineering, pp. 84-93 (2007).

[10]

Lung, J., Aranda, J., Easterbrook, S.M., Wilson, G.V.: On the difficulty of replicating human subjects studies in software engineering. In: ICSE '08: Proceedings of the 30th International Conference on Software Engineering, pp. 191-200 (2008).

Digital Library

[11]

Miller, G.A.: The magical number seven. plus or minus two: Some limits on our capacity for processing information. Psychol. Rev. 63, 81-97 (1956). http://users.ecs.soton.ac.uk/~harnad/Papers/Py104/ Miller/miller.html

[12]

Reiss, S.P.: Semantics-based code search. In: ICSE '09: Proceedings of the 31st International Conference on Software Engineering, pp. 243-253 (2009).

Digital Library

[13]

Robbes, R.: Mining a change-based software repository. In: MSR '07: Proceedings of Fourth International Workshop on Mining Software Repositories, p. 15. ACM Press, New York (2007).

[14]

Robbes, R., Lanza, M.: An approach to software evolution based on semantic change. In: FASE '07: Proceedings of the 10th International Conference on Fundamental Approaches to Software Engineering, pp. 27-41 (2007a).

Digital Library

[15]

Robbes, R., Lanza, M.: Characterizing and understanding development sessions. In: ICPC '07: Proceedings of the 15th International Conference on Program Comprehension, pp. 155-164 (2007b).

Digital Library

[16]

Robbes, R., Lanza, M.: Spyware: a change-aware development toolset. In: ICSE '08: Proceedings of the 30th International Conference on Software Engineering, pp. 847-850 (2008).

[17]

Sharon, Y.: Eclipseye--spying on eclipse. Bachelor's thesis, University of Lugano (2007).

[18]

Sim, S.E., Easterbrook, S.M., Holt, R.C.: Using benchmarking to advance research: A challenge to software engineering. In: ICSE '03: Proceedings of the 25th International Conference on Software Engineering, pp. 74-83 (2003).

Digital Library

[19]

Sim, S.E., Holt, R.C., Easterbrook, S.: On using a benchmark to evaluate C++ extractors. In: IWPC '02: Proceedings of the 10th International Workshop on Program Comprehension, p. 114. IEEE Computer Society, Washington (2002).

[20]

Wuyts, R.: Roeltyper: a fast type reconstructor for smalltalk. http://decomp.ulb.ac.be/roelwuyts/smalltalk/ roeltyper/ (2007).

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Weber TBrandmaier MSchmidt AMayer S(2024)Significant Productivity Gains through Programming with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36611458:EICS(1-29)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3661145
Liu FFu ZLi GJin ZLiu HHao YZhang L(2024)Non-Autoregressive Line-Level Code CompletionACM Transactions on Software Engineering and Methodology10.1145/364959433:5(1-34)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649594
Ciniselli MMartin-Lopez ABavota GBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)On the Generalizability of Deep Learning-based Code Completion Across Programming Language VersionsProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644411(99-111)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644411
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Published In

cover image Automated Software Engineering

Automated Software Engineering Volume 17, Issue 2

June 2010

96 pages

ISSN:0928-8910

Issue’s Table of Contents

Copyright © Copyright © 2010 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2010

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Cited By

Weber TBrandmaier MSchmidt AMayer S(2024)Significant Productivity Gains through Programming with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36611458:EICS(1-29)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3661145
Liu FFu ZLi GJin ZLiu HHao YZhang L(2024)Non-Autoregressive Line-Level Code CompletionACM Transactions on Software Engineering and Methodology10.1145/364959433:5(1-34)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649594
Ciniselli MMartin-Lopez ABavota GBaysal OLinares-Vasquez MMoran KSteinmacher I(2024)On the Generalizability of Deep Learning-based Code Completion Across Programming Language VersionsProceedings of the 32nd IEEE/ACM International Conference on Program Comprehension10.1145/3643916.3644411(99-111)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643916.3644411
Adhikari BRapos EStephan M(2024)SimIMA: a virtual Simulink intelligent modeling assistantSoftware and Systems Modeling (SoSyM)10.1007/s10270-023-01093-623:1(29-56)Online publication date: 1-Feb-2024
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Kim KGhatpande SKim DZhou XLiu KBissyandé TKlein JLe Traon Y(2023)Big Code Search: A BibliographyACM Computing Surveys10.1145/360490556:1(1-49)Online publication date: 26-Aug-2023
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Wang SWen MLin BLiu YBissyandé TMao X(2023)Pre-implementation Method Name Prediction for Object-oriented ProgrammingACM Transactions on Software Engineering and Methodology10.1145/359720332:6(1-35)Online publication date: 29-Sep-2023
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Ciniselli MPascarella LAghajani EScalabrino SOliveto RBavota GGrundy JPollock LPenta M(2023)Source Code Recommender Systems: The Practitioners' PerspectiveProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00182(2161-2172)Online publication date: 14-May-2023
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Mastropaolo APascarella LGuglielmi ECiniselli MScalabrino SOliveto RBavota GGrundy JPollock LPenta M(2023)On the Robustness of Code Generation Techniques: An Empirical Study on GitHub CopilotProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00181(2149-2160)Online publication date: 14-May-2023
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Nie PBanerjee RLi JMooney RGligoric MGrundy JPollock LPenta M(2023)Learning Deep Semantics for Test CompletionProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00178(2111-2123)Online publication date: 14-May-2023
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