research-article

Learning to find naming issues with big code and small supervision

Authors:

Cheng-Chun Lee,

Veselin Raychev,

Martin VechevAuthors Info & Claims

PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

Pages 296 - 311

https://doi.org/10.1145/3453483.3454045

Published: 18 June 2021 Publication History

Abstract

We introduce a new approach for finding and fixing naming issues in source code. The method is based on a careful combination of unsupervised and supervised procedures: (i) unsupervised mining of patterns from Big Code that express common naming idioms. Program fragments violating such idioms indicates likely naming issues, and (ii) supervised learning of a classifier on a small labeled dataset which filters potential false positives from the violations.

We implemented our method in a system called Namer and evaluated it on a large number of Python and Java programs. We demonstrate that Namer is effective in finding naming mistakes in real world repositories with high precision (~70%). Perhaps surprisingly, we also show that existing deep learning methods are not practically effective and achieve low precision in finding naming issues (up to ~16%).

References

[1]

2020. American fuzzy lop. https://lcamtuf.coredump.cx/afl/

[2]

2020. Error (Java SE 14 & JDK 14). https://docs.oracle.com/en/java/javase/14/docs/api/java.base/java/lang/Error.html

[3]

2020. GitHub. https://github.com

[4]

2020. ICLR20-Great. https://github.com/VHellendoorn/ICLR20-Great

[5]

2020. tf-gnn-samples. https://github.com/microsoft/tf-gnn-samples

[6]

2020. unittest — Unit testing framework. https://docs.python.org/3/library/unittest.html##unittest.TestCase.assertTrue

[7]

Miltiadis Allamanis, Earl T. Barr, Christian Bird, and Charles Sutton. 2015. Suggesting accurate method and class names. In FSE 2015. https://doi.org/10.1145/2786805.2786849

Digital Library

[8]

Miltiadis Allamanis, Earl T. Barr, Christian Bird, and Charles A. Sutton. 2014. Learning Natural Coding Conventions. In FSE 2014. https://doi.org/10.1145/2635868.2635883

Digital Library

[9]

Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to Represent Programs with Graphs. In ICLR 2018. https://openreview.net/forum?id=BJOFETxR-

[10]

Miltiadis Allamanis, Hao Peng, and Charles A. Sutton. 2016. A Convolutional Attention Network for Extreme Summarization of Source Code. In ICML 2016. http://proceedings.mlr.press/v48/allamanis16.html

[11]

Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. In ICLR 2019. https://openreview.net/forum?id=H1gKYo09tX

[12]

Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2018. A General Path-Based Representation for Predicting Program Properties. In PLDI 2018. https://doi.org/10.1145/3192366.3192412

Digital Library

[13]

Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. code2vec: Learning Distributed Representations of Code. Proc. ACM Program. Lang., 3, POPL (2019), 40:1–40:29. https://doi.org/10.1145/3290353

Digital Library

[14]

Osbert Bastani, Rahul Sharma, Alex Aiken, and Percy Liang. 2018. Active Learning of Points-to Specifications. In PLDI 2018. https://doi.org/10.1145/3192366.3192383

Digital Library

[15]

Benjamin Bichsel, Veselin Raychev, Petar Tsankov, and Martin Vechev. 2016. Statistical Deobfuscation of Android Applications. In CCS 2016. https://doi.org/10.1145/2976749.2978422

Digital Library

[16]

Pavol Bielik and Martin Vechev. 2020. Adversarial Robustness for Code. In ICML. http://proceedings.mlr.press/v119/bielik20a.html

[17]

Marcel B�hme, Van-Thuan Pham, and Abhik Roychoudhury. 2016. Coverage-based Greybox Fuzzing as Markov Chain. In CCS 2016. https://doi.org/10.1145/2976749.2978428

Digital Library

[18]

Simon Butler, Michel Wermelinger, Yijun Yu, and Helen Sharp. 2010. Exploring the Influence of Identifier Names on Code Quality: An Empirical Study. In CSMR 2010. https://doi.org/10.1109/CSMR.2010.27

Digital Library

[19]

Cristian Cadar, Daniel Dunbar, and Dawson R. Engler. 2008. KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs. In OSDI 2008. http://www.usenix.org/events/osdi08/tech/full_papers/cadar/cadar.pdf

Digital Library

[20]

Victor Chibotaru, Benjamin Bichsel, Veselin Raychev, and Martin Vechev. 2019. Scalable Taint Specification Inference with Big Code. In PLDI 2019. https://doi.org/10.1145/3314221.3314648

Digital Library

[21]

Patrick Cousot and Radhia Cousot. 1977. Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints. In POPL 1977. https://doi.org/10.1145/512950.512973

Digital Library

[22]

Jan Eberhardt, Samuel Steffen, Veselin Raychev, and Martin Vechev. 2019. Unsupervised Learning of API Aliasing Specifications. In PLDI 2019. https://doi.org/10.1145/3314221.3314640

Digital Library

[23]

Patrice Godefroid, Hila Peleg, and Rishabh Singh. 2017. Learn&Fuzz: Machine Learning for Input Fuzzing. In ASE 2017. https://doi.org/10.1109/ASE.2017.8115618

[24]

Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining Frequent Patterns without Candidate Generation. In SIGMOD 2000. https://doi.org/10.1145/342009.335372

Digital Library

[25]

Quinn Hanam, Lin Tan, Reid Holmes, and Patrick Lam. 2014. Finding patterns in static analysis alerts: improving actionable alert ranking. In MSR 2014. https://doi.org/10.1145/2597073.2597100

Digital Library

[26]

Jingxuan He, Mislav Balunovic, Nodar Ambroladze, Petar Tsankov, and Martin Vechev. 2019. Learning to Fuzz from Symbolic Execution with Application to Smart Contracts. In CCS 2019. https://doi.org/10.1145/3319535.3363230

Digital Library

[27]

Jingxuan He, Pesho Ivanov, Petar Tsankov, Veselin Raychev, and Martin Vechev. 2018. Debin: Predicting Debug Information in Stripped Binaries. In CCS 2018. https://doi.org/10.1145/3243734.3243866

Digital Library

[28]

Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber. 2020. Global Relational Models of Source Code. In ICLR 2020. OpenReview.net. https://openreview.net/forum?id=B1lnbRNtwr

[29]

Kihong Heo, Hakjoo Oh, and Hongseok Yang. 2019. Resource-aware Program Analysis via Online Abstraction Coarsening. In ICSE 2019. https://doi.org/10.1109/ICSE.2019.00027

Digital Library

[30]

Einar W. Høst and Bjarte M. Ø stvold. 2009. Debugging Method Names. In ECOOP 2009. https://doi.org/10.1007/978-3-642-03013-0_14

Digital Library

[31]

Ted Kremenek and Dawson R. Engler. 2003. Z-Ranking: using statistical analysis to counter the impact of static analysis approximations. In SAS 2003. https://doi.org/10.1007/3-540-44898-5_16

[32]

Carson Kai-Sang Leung, Laks V. S. Lakshmanan, and Raymond T. Ng. 2002. Exploiting Succinct Constraints using FP-trees. SIGKDD Explorations, 4, 1 (2002), 40–49. https://doi.org/10.1145/568574.568581

Digital Library

[33]

Yi Li, Shaohua Wang, Tien N. Nguyen, and Son Van Nguyen. 2019. Improving Bug Detection via Context-Based Code Representation Learning and Attention-Based Neural Networks. Proc. ACM Program. Lang., 3, OOPSLA (2019), 162:1–162:30. https://doi.org/10.1145/3360588

Digital Library

[34]

Hui Liu, Qiurong Liu, Cristian-Alexandru Staicu, Michael Pradel, and Yue Luo. 2016. Nomen est omen: Exploring and Exploiting Similarities between Argument and Parameter Names. In ICSE 2016. https://doi.org/10.1145/2884781.2884841

Digital Library

[35]

Cristina V. Lopes, Petr Maj, Pedro Martins, Vaibhav Saini, Di Yang, Jakub Zitny, Hitesh Sajnani, and Jan Vitek. 2017. DéjàVu: a Map of Code Duplicates on GitHub. Proc. ACM Program. Lang., 1, OOPSLA (2017), 84:1–84:28. https://doi.org/10.1145/3133908

Digital Library

[36]

Hakjoo Oh, Hongseok Yang, and Kwangkeun Yi. 2015. Learning a Strategy for Adapting a Program Analysis via Bayesian Optimisation. In OOPSLA 2015. https://doi.org/10.1145/2814270.2814309

Digital Library

[37]

Rumen Paletov, Petar Tsankov, Veselin Raychev, and Martin Vechev. 2018. Inferring crypto API rules from code changes. In PLDI 2018. https://doi.org/10.1145/3192366.3192403

Digital Library

[38]

Michael Pradel and Thomas R. Gross. 2011. Detecting Anomalies in the Order of Equally-typed Method Arguments. In ISSTA 2011. https://doi.org/10.1145/2001420.2001448

Digital Library

[39]

Michael Pradel and Koushik Sen. 2018. DeepBugs: a Learning Approach to Name-based Bug Detection. Proc. ACM Program. Lang., 2, OOPSLA (2018), 147:1–147:25. https://doi.org/10.1145/3276517

Digital Library

[40]

Veselin Raychev, Martin Vechev, and Andreas Krause. 2015. Predicting Program Properties from "Big Code". In POPL 2015. https://doi.org/10.1145/2676726.2677009

Digital Library

[41]

Andrew Rice, Edward Aftandilian, Ciera Jaspan, Emily Johnston, Michael Pradel, and Yulissa Arroyo-Paredes. 2017. Detecting Argument Selection Defects. Proc. ACM Program. Lang., 1, OOPSLA (2017), 104:1–104:22. https://doi.org/10.1145/3133928

Digital Library

[42]

Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, and Suman Jana. 2019. NEUZZ: Efficient Fuzzing with Neural Program Smoothing. In S&P 2019. https://doi.org/10.1109/SP.2019.00052

[43]

Gagandeep Singh, Markus Püschel, and Martin Vechev. 2018. Fast Numerical Program Analysis with Reinforcement Learning. In CAV 2018. https://doi.org/10.1007/978-3-319-96145-3_12

[44]

Yannis Smaragdakis and George Balatsouras. 2015. Pointer Analysis. Found. Trends Program. Lang., 2, 1 (2015), 1–69. https://doi.org/10.1561/2500000014

Digital Library

[45]

Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh. 2019. Neural Program Repair by Jointly Learning to Localize and Repair. In ICLR 2019. https://openreview.net/forum?id=ByloJ20qtm

[46]

Junjie Wang, Bihuan Chen, Lei Wei, and Yang Liu. 2017. Skyfire: Data-Driven Seed Generation for Fuzzing. In S&P 2017. https://doi.org/10.1109/SP.2017.23

[47]

Yu Wang, Ke Wang, Fengjuan Gao, and Linzhang Wang. 2020. Learning semantic program embeddings with graph interval neural network. Proc. ACM Program. Lang., 4, OOPSLA (2020), 137:1–137:27. https://doi.org/10.1145/3428205

Digital Library

Cited By

Wang CZhang JWu RZhang C(2024)DAInfer: Inferring API Aliasing Specifications from Library Documentation via Neurosymbolic OptimizationProceedings of the ACM on Software Engineering10.1145/36608161:FSE(2469-2492)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660816
Wang JLi LLiu KDu X(2024)Detecting and explaining Python name errorsInformation and Software Technology10.1016/j.infsof.2024.107592(107592)Online publication date: Oct-2024
https://doi.org/10.1016/j.infsof.2024.107592
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
https://dl.acm.org/doi/10.1145/3597203
Show More Cited By

Index Terms

Learning to find naming issues with big code and small supervision
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Program analysis

Recommendations

DeepBugs: a learning approach to name-based bug detection

Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information and therefore miss some classes of bugs. The few existing name-based bug ...
Scalable and systematic detection of buggy inconsistencies in source code
OOPSLA '10

Software developers often duplicate source code to replicate functionality. This practice can hinder the maintenance of a software project: bugs may arise when two identical code segments are edited inconsistently. This paper presents DejaVu, a highly ...
Scalable and systematic detection of buggy inconsistencies in source code
OOPSLA '10: Proceedings of the ACM international conference on Object oriented programming systems languages and applications

Software developers often duplicate source code to replicate functionality. This practice can hinder the maintenance of a software project: bugs may arise when two identical code segments are edited inconsistently. This paper presents DejaVu, a highly ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

June 2021

1341 pages

ISBN:9781450383912

DOI:10.1145/3453483

General Chair:
Stephen N. Freund
Williams College, USA
,
Program Chair:
Eran Yahav
Technion, Israel

Copyright � 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGPLAN: ACM Special Interest Group on Programming Languages

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

PLDI '21

Sponsor:

SIGPLAN

PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation

June 20 - 25, 2021

Virtual, Canada

Acceptance Rates

Overall Acceptance Rate 406 of 2,067 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
312
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)4

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang CZhang JWu RZhang C(2024)DAInfer: Inferring API Aliasing Specifications from Library Documentation via Neurosymbolic OptimizationProceedings of the ACM on Software Engineering10.1145/36608161:FSE(2469-2492)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660816
Wang JLi LLiu KDu X(2024)Detecting and explaining Python name errorsInformation and Software Technology10.1016/j.infsof.2024.107592(107592)Online publication date: Oct-2024
https://doi.org/10.1016/j.infsof.2024.107592
Wang SWen MLin BLiu YBissyand� 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
https://dl.acm.org/doi/10.1145/3597203
Bertolotti FCazzola W(2023)CombTransformers: Statement-Wise Transformers for Statement-Wise RepresentationsIEEE Transactions on Software Engineering10.1109/TSE.2023.331079349:10(4677-4690)Online publication date: 6-Sep-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3310793
Cheng XZhang GWang HSui YRyu SSmaragdakis Y(2022)Path-sensitive code embedding via contrastive learning for software vulnerability detectionProceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3533767.3534371(519-531)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1145/3533767.3534371
Patra JPradel MDwyer MDamian DZeller A(2022)NalinProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510144(1469-1481)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510144

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents