How Program History Can Improve Code Completion
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 always comes in alphabetic order, i.e., the environment is only second-guessing the name based on pattern matching. Finding the correct candidate can be cumbersome or slower than typing the full name. We present an approach to improve code completion with program history. We define a benchmark measuring the accuracy and usefulness of a code completion engine. Further, we use the change history data to also improve the results offered by code completion tools. Finally, we propose an alternative interface for completion tools.
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In Py150-wilds, the task focuses on class name and method name prediction because developer studies indicate these are used most commonly by developers.
“To define a task closer to real applications, in Py150-wilds we focus on class name and method name prediction (which are used most commonly by developers).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Robbes and Lanza, 2008
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Sasano, I., & Choi, K. (2020) · ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM) · doi:10.1145/3441296.3441395 · Cited by 6 · see citing works
An approach to completing variable names for implicitly typed functional languages
Sasano, I., & Goto, T. (2012) · LISP and Symbolic Computation · doi:10.1007/s10990-013-9095-x · Cited by 6 · see citing works
Source Code Similarity and Clone Search
Keivanloo, I. (2013) · Spectrum Research Repository (Concordia University) · Cited by 6 · see citing works
Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling Scripts
Zhou, Y., Cai, X., Shi, Q., Huang, Y., Li, H., Qu, H., Weng, D., & Wu, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714239 · Cited by 4 · see citing works
Investigating Human Performance in Hand-Drawn Symbol Autocompletion
Costagliola, G., Rosa, M. D., & Fuccella, V. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.54 · Cited by 4 · see citing works
PERSONA: A personalized model for code recommendation
Nguyen, T. T., & Nguyen, T. T. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259834 · Cited by 4 · see citing works
Visual Studio Code Extension and Auto-completion for Mizar Language
Taniguchi, H., & Nakasho, K. (2021) · International Symposium on Computing and Networking · doi:10.1109/candar53791.2021.00033 · Cited by 4 · see citing works
- Authors
- Robbes, R., & Lanza, M.
- Affiliations
- Università della Svizzera italiana (CH)
- Venue
- 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 317-326
- OpenAlex
- W2166597811
- Cited by
- 155
- References
- 9
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 11 · 2023: 9 · 2022: 6 · 2021: 7 · 2020: 12 · 2019: 11 · 2018: 4 · 2017: 7 · 2016: 9 · 2015: 10 · 2014: 14 · 2013: 6 · 2012: 14
- FWCI
- 6.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM International Conference on Automated Software Engineering (ASE) (publishedVersion)
- Open Research (University of Surrey) (submittedVersion)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (115990)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- code completion, program history, change history, developer productivity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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