The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Version models for software configuration management
Conradi, R., & Westfechtel, B. (1998) · ACM Computing Surveys · doi:10.1145/280277.280280 · Cited by 587
Using task context to improve programmer productivity
Kersten, M., & Murphy, G. C. (2006) · Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering · doi:10.1145/1181775.1181777 · Cited by 452
How are Java software developers using the Eclipse IDE?
Murphy, G. C., Kersten, M., & Findlater, L. (2006) · IEEE Software · doi:10.1109/ms.2006.105 · Cited by 413
Little, G., & Miller, R. C. (2007) · doi:10.1145/1321631.1321646 · Cited by 96
Robbes, R., & Lanza, M. (2008) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/1368088.1368219 · Cited by 81
Self-citationMining a Change-Based Software Repository
Robbes, R. (2007) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2007.18 · Cited by 58
Self-citationCharacterizing and Understanding Development Sessions
Robbes, R., & Lanza, M. (2007) · 15th IEEE International Conference on Program Comprehension (ICPC '07) · doi:10.1109/icpc.2007.12 · Cited by 51
Self-citationLittle, G., & Miller, R. C. (2008) · Automated Software Engineering · doi:10.1007/s10515-008-0041-9 · Cited by 48
An Approach to Software Evolution Based on Semantic Change
Robbes, R., Lanza, M., & Lungu, M. F. (2007) · Springer eBooks · doi:10.1007/978-3-540-71289-3_4 · Cited by 37
Self-citation
Research works printed but not linked: 2
- sharon, 2007, Eclipseye - spying on eclipse
- wuyts, 2007, Roeltyper A fast type reconstructor for Smalltalk
Cited by
Sorted by citations
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043
Learning from examples to improve code completion systems
Bruch, M., Monperrus, M., & Mezini, M. (2009) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/1595696.1595728 · Cited by 391
Learning natural coding conventions
Allamanis, M., Barr, E. T., Bird, C., & Sutton, C. A. (2014) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2635868.2635883 · Cited by 380
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106290 · Cited by 292
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
A statistical semantic language model for source code
Nguyen, T. T., Nguyen, A. T., Nguyen, H. A., & Nguyen, T. N. (2013) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2491411.2491458 · Cited by 219
API code recommendation using statistical learning from fine-grained changes
Nguyen, A. T., Hilton, M. L., Codoban, M., Nguyen, H. A., Mast, L., Rademacher, E., Nguyen, T. N., & Dig, D. (2016) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2950290.2950333 · Cited by 167
Micro interaction metrics for defect prediction
Lee, T., Nam, J., Han, D., Kim, S., & In, H. P. (2011) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2025113.2025156 · Cited by 152
Graph-based statistical language model for code
Nguyen, A. T., & Nguyen, T. N. (2016) · Cited by 142
Aroma: code recommendation via structural code search
Luan, S., Yang, D., Barnaby, C., Sen, K., & Chandra, S. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3360578 · Cited by 138
Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review
Wong, M.-F., Guo, S., Hang, C. N., Ho, S., & Tan, C. W. (2023) · Entropy · doi:10.3390/e25060888 · Cited by 169
Graph-Based Statistical Language Model for Code
Nguyen, A. T., & Nguyen, T. N. (2015) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2015.336 · Cited by 119
Buschek, D., Zürn, M., & Eiband, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445372 · Cited by 108
Graph-based pattern-oriented, context-sensitive source code completion
Nguyen, A. T., Nguyen, T. T., Nguyen, H. A., Tamrawi, A., Nguyen, H. V., Al-Kofahi, J. M., & Nguyen, T. N. (2012) · doi:10.5555/2337223.2337232 · Cited by 104
Omar, C., Yoon, Y., LaToza, T. D., & Myers, B. A. (2012) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2012.6227133 · Cited by 94
Graph-based pattern-oriented, context-sensitive source code completion
Nguyen, A. T., Nguyen, T. T., Nguyen, H. A., Tamrawi, A., Nguyen, H. V., Al-Kofahi, J. M., & Nguyen, T. N. (2012) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2012.6227205 · Cited by 147
CodeFill: multi-token code completion by jointly learning from structure and naming sequences
Izadi, M., Gismondi, R., & Gousios, G. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510172 · Cited by 86
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning
Liu, F., Li, G., Wei, B., Xia, X., Fu, Z., & Jin, Z. G. (2020) · International Conference on Program Comprehension · doi:10.1145/3387904.3389261 · Cited by 88
Automatic mining of specifications from invocation traces and method invariants
Krka, I., Brun, Y., & Medvidović, N. (2014) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2635868.2635890 · Cited by 81
When Code Completion Fails: A Case Study on Real-World Completions
Hellendoorn, V. J., Proksch, S., Gall, H. C., & Bacchelli, A. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2019.00101 · Cited by 76
Dilhara, M., Ketkar, A., & Dig, D. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3453478 · Cited by 76
Code Completion from Abbreviated Input
Han, S., Wallace, D. R., & Miller, R. C. (2009) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2009.64 · Cited by 71
An evaluation of the strategies of sorting, filtering, and grouping API methods for Code Completion
Hou, D., & Pletcher, D. M. (2011) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2011.6080790 · Cited by 72
Omar, C., Yoon, Y., LaToza, T. D., & Myers, B. A. (2012) · doi:10.5555/2337223.2337324 · Cited by 68
Learning API usages from bytecode
Nguyen, T. T., Pham, H. V., Vu, P. M., & Nguyen, T. T. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884873 · Cited by 65
Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns
Nguyen, H. A., Nguyen, T. N., Dig, D., Nguyen, S., Tran, H., & Hilton, M. L. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00089 · Cited by 64
Significant Productivity Gains through Programming with Large Language Models
Weber, T., Brandmaier, M., Schmidt, A., & Mayer, S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3661145 · Cited by 64
“…and industry have explored different mechanisms how these suggestions and code completions can be generated, from semantic analysis of the source code [3, 22, 28, 48, 49] to machine learning approaches [43, 57] Programming with Large Language Models 256:3 and different combinations [7, 47].”
BackgroundSpeculative analysis of integrated development environment recommendations
Muşlu, K., Brun, Y., Holmes, R., Ernst, M. D., & Notkin, D. S. (2012) · Conference on Object-Oriented Programming Systems, Languages, and Applications · doi:10.1145/2384616.2384665 · Cited by 59
CSCC: Simple, Efficient, Context Sensitive Code Completion
Asaduzzaman, M., Roy, C. K., Schneider, K. A., & Hou, D. (2014) · 2014 IEEE International Conference on Software Maintenance and Evolution · doi:10.1109/icsme.2014.29 · Cited by 55
Effective API recommendation without historical software repositories
Liu, X., Huang, L., & Ng, V. (2018) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3238147.3238216 · Cited by 55
Using Structure-Based Recommendations to Facilitate Discoverability in APIs
Duala-Ekoko, E., & Robillard, M. P. (2012) · Cited by 50
Recommending API Usages for Mobile Apps with Hidden Markov Model
Nguyen, T. T., Pham, H. V., Vu, P. M., & Nguyen, T. T. (2015) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2015.109 · Cited by 50
Rahman, M., Palani, D., & Rigby, P. C. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00022 · Cited by 47
Towards a better code completion system by API grouping, filtering, and popularity-based ranking
Hou, D., & Pletcher, D. M. (2010) · International Workshop on Recommendation Systems for Software Engineering (RSSE) · doi:10.1145/1808920.1808926 · Cited by 45
A deep neural network language model with contexts for source code
Nguyen, A. T., Nguyen, T. D., Phan, H., & Nguyen, T. N. (2018) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2018.8330220 · Cited by 45
Exploring API method parameter recommendations
Asaduzzaman, M., Roy, C. K., Monir, S., & Schneider, K. A. (2015) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2015.7332473 · Cited by 34
AI-Assisted Code Authoring at Scale: Fine-Tuning, Deploying, and Mixed Methods Evaluation
Murali, V., Maddila, C., Ahmad, I., Bolin, M., Cheng, D., Ghorbani, N., Fernandez, R., Nagappan, N., & Rigby, P. C. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3643774 · Cited by 91
Generating API Call Rules from Version History and Stack Overflow Posts
Azad, S., Rigby, P. C., & Guerrouj, L. (2017) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2990497 · Cited by 33
Combining Program Analysis and Statistical Language Model for Code Statement Completion
Nguyen, S., Nguyen, T. N., Li, Y., & Wang, S. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00072 · Cited by 31
Bruch, M., Bodden, E., Monperrus, M., & Mezini, M. (2010) · FSE/SDP Workshop on Future of Software Engineering Research (FoSER) · doi:10.1145/1882362.1882374 · Cited by 30
The Influence of the Task on Programmer Behaviour
Ying, A. T. T., & Robillard, M. P. (2011) · 2011 IEEE 19th International Conference on Program Comprehension · doi:10.1109/icpc.2011.35 · Cited by 41
Replaying IDE interactions to evaluate and improve change prediction approaches
Robbes, R., Pollet, D., & Lanza, M. (2010) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2010.5463278 · Cited by 28
Self-citationRobbes, R. (2009) · reroDoc Digital Library · Cited by 28
Self-citationUsing Structure-Based Recommendations to Facilitate Discoverability in APIs
Duala-Ekoko, E., & Robillard, M. P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22655-7_5 · Cited by 28
RLCoder: Reinforcement Learning for Repository-Level Code Completion
Wang, Y., Wang, Y., Guo, D., Chen, J., Zhang, R., Ma, Y., & Zheng, Z. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00014 · Cited by 43
A unified multi-task learning model for AST-level and token-level code completion
Liu, F., Li, G., Wei, B., Xia, X., Fu, Z., & Jin, Z. G. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10140-7 · Cited by 28
Autumn Leaves: Curing the Window Plague in IDEs
Roethlisberger, D., Nierstrasz, O., & Ducasse, S. (2009) · Working Conference on Reverse Engineering (WCRE) · doi:10.1109/wcre.2009.18 · Cited by 27
Evaluating the evaluations of code recommender systems: a reality check
Proksch, S., Amann, S., Nadi, S., & Mezini, M. (2016) · Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/2970276.2970330 · Cited by 26
Suggesting API Usage to Novice Programmers with the Example Guru
Ichinco, M., Hnin, W. Y., & Kelleher, C. L. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025827 · Cited by 26
How Practitioners Expect Code Completion?
Wang, C., Hu, J., Gao, C., Jin, Y., Xie, T., Huang, H., Lei, Z., & Deng, Y. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616280 · Cited by 50
GraPacc: a graph-based pattern-oriented, context-sensitive code completion tool
Nguyen, A. T., Nguyen, H. A., Nguyen, T. T., & Nguyen, T. N. (2012) · doi:10.5555/2337223.2337431 · Cited by 25
GraPacc: A graph-based pattern-oriented, context-sensitive code completion tool
Nguyen, A. T., Nguyen, H. A., Nguyen, T. T., & Nguyen, T. N. (2012) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2012.6227236 · Cited by 25
Collective Intelligence for Smarter API Recommendations in Python
D'Souza, A. R., Yang, D., & Lopes, C. V. (2016) · IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM) · doi:10.1109/scam.2016.22 · Cited by 23
Speculative analysis of integrated development environment recommendations
Muşlu, K., Brun, Y., Holmes, R., Ernst, M. D., & Notkin, D. S. (2012) · ACM SIGPLAN Notices · doi:10.1145/2398857.2384665 · Cited by 23
Fast type reconstruction for dynamically typed programming languages
Pluquet, F., Marot, A., & Wuyts, R. (2009) · Symposium on Dynamic Languages (DLS) · doi:10.1145/1640134.1640145 · Cited by 22
Wassink, I. (2009) · doi:10.3990/1.9789036529327 · Cited by 22
Learning API Usages from Bytecode: A Statistical ApproachPreprint
Nguyen, T. T., Pham, H. V., Vu, P. M., & Nguyen, T. T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.07306 · Cited by 22
D’Ambros, M., Lanza, M., & Robbes, R. (2010) · doi:10.1145/1809198.1809204 · Cited by 21
Self-citationSupporting task-oriented navigation in IDEs with configurable HeatMaps
Röthlisberger, D., Nierstrasz, O., Ducasse, S., Pollet, D., & Robbes, R. (2009) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2009.5090052 · Cited by 21
Self-citationA Simple, Efficient, Context‐sensitive Approach for Code Completion
Asaduzzaman, M., Roy, C. K., Schneider, K. A., & Hou, D. (2016) · Journal of Software Evolution and Process · doi:10.1002/smr.1791 · Cited by 20
An environment for synchronous software development
Hattori, L., & Lanza, M. (2009) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion.2009.5070987 · Cited by 19
Self-citationStudents' Perspectives on AI Code Completion: Benefits and Challenges
Takerngsaksiri, W., Warusavitarne, C., Yaacoub, C., Keng Hou, M. H., & Tantithamthavorn, C. (2024) · 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00252 · Cited by 18
Context-Sensitive Code Completion Tool for Better API Usability
Asaduzzaman, M., Roy, C. K., Schneider, K. A., & Hou, D. (2014) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme.2014.110 · Cited by 18
Hinkel, G., Goldschmidt, T., Burger, E., & Reussner, R. (2017) · Software & Systems Modeling · doi:10.1007/s10270-017-0578-9 · Cited by 17
Changes as First-Class Citizens
Soetens, Q. D., Robbes, R., & Demeyer, S. (2017) · ACM Computing Surveys · doi:10.1145/3038926 · Cited by 16
Self-citationCode completion of multiple keywords from abbreviated input
Han, S., Wallace, D. R., & Miller, R. C. (2011) · Automated Software Engineering · doi:10.1007/s10515-011-0083-2 · Cited by 14
Multi-line AI-Assisted Code Authoring
Dunay, O., Cheng, D., Tait, A., Thakkar, P., Rigby, P. C., Chiu, A., Ahmad, I., Ganesan, A., Maddila, C., Murali, V., et al. (2024) · SIGSOFT FSE Companion · doi:10.1145/3663529.3663836 · Cited by 29
Syntax-aware on-the-fly code completion
Takerngsaksiri, W., Tantithamthavorn, C. K., & Li, Y.-F. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107336 · Cited by 14
Designing Workflows on the Fly Using e-BioFlow
Wassink, I., Ooms, M., & van der Vet, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10383-4_35 · Cited by 14
Towards standardized evaluation of developer-assistance tools
Proksch, S., Amann, S., & Mezini, M. (2014) · International Workshop on Recommendation Systems for Software Engineering (RSSE) · doi:10.1145/2593822.2593827 · Cited by 13
All you need is logs: improving code completion by learning from anonymous IDE usage logs
Bibaev, V., Kalina, A., Lomshakov, V., Golubev, Y., Bezzubov, A., Povarov, N. I., & Bryksin, T. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3558968 · Cited by 13
Proactive modeling: a new model intelligence technique
Pati, T., Kolli, S., & Hill, J. H. (2015) · Software & Systems Modeling · doi:10.1007/s10270-015-0465-1 · Cited by 13
Principled syntactic code completion using placeholders
Amorim, L. E. D. S., Erdweg, S. T., Wachsmuth, G. H., & Eelco Visser, B. (2016) · ACM SIGPLAN International Conference on Software Language Engineering (SLE) · doi:10.1145/2997364.2997374 · Cited by 13
From Triumph to Uncertainty: The Journey of Software Engineering in the AI Era
Mastropaolo, A., Escobar‐Velásquez, C., & Linares‐Vásquez, M. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3709360 · Cited by 23
Costagliola, G., Rosa, M. D., & Fuccella, V. (2014) · Computers & Graphics · doi:10.1016/j.cag.2013.12.003 · Cited by 12
Capturing and Exploiting IDE Interactions
Gu, Z., Schleck, D., Barr, E. T., & Su, Z. (2014) · ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) · doi:10.1145/2661136.2661144 · Cited by 11
Source Code Recommender Systems: The Practitioners' Perspective
Ciniselli, M., Pascarella, L., Aghajani, E., Scalabrino, S., Oliveto, R., & Bavota, G. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00182 · Cited by 13
An empirical investigation of code completion usage by professional software developers.
Mărăşoiu, M., Church, L., & Blackwell, A. F. (2015) · PPIG · Cited by 11
Heuristic and Neural Network Based Prediction of Project-Specific API Member Access
Jiang, L. C., Liu, H., Jiang, H., Zhang, L., & Hong, M. (2020) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3017794 · Cited by 11
Omar, C., Yoon, Y., LaToza, T. D., & Myers, B. A. (2011) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2011.6070422 · Cited by 10
Agile software assessment (Invited paper)
Nierstrasz, O., & Lungu, M. F. (2012) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2012.6240507 · Cited by 9
Bajaj, K., Pattabiraman, K., & Mesbah, A. (2014) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/2642937.2642981 · Cited by 9
Deep-AutoCoder: Learning to Complete Code Precisely with Induced Code Tokens
Hu, X., Men, R., Li, G., & Jin, Z. G. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.00030 · Cited by 9
Sequential coding patterns: How to use them effectively in code recommendation
da Silva, L. L. N., Kohwalter, T. C., Plastino, A., & Murta, L. (2021) · Information and Software Technology · doi:10.1016/j.infsof.2021.106690 · Cited by 8
Vertical Code Completion: Going Beyond the Current Ctrl+Space
da Silva, L. L. N., de Oliveira, T. G., Plastino, A., & Murta, L. (2012) · Brazilian Symposium on Software Components, Architectures, and Reuse (SBCARS) · doi:10.1109/sbcars.2012.22 · Cited by 7
Cookbook: in situ code completion using edit recipes learned from examples
Jacobellis, J., Meng, N., & Kim, M. (2014) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2591062.2591076 · Cited by 7
SmartGroups: Focusing on Task-Relevant Source Artifacts in IDEs
Röthlisberger, D., Nierstrasz, O., & Ducasse, S. (2011) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc.2011.20 · Cited by 6
An Integrative Human-Centered Architecture for Interactive Programming Assistants
Blinn, A., Moon, D., Griffis, E., & Omar, C. (2022) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc53370.2022.9833110 · Cited by 6
A Benchmark for Change Prediction
Robbes, R., Lanza, M., & Pollet, D. (2008) · reroDoc Digital Library · Cited by 6
Self-citationScope-aware Code Completion with Discriminative Modeling
Sheng, H., Xiao, C., & Ishikawa, Y. (2019) · Journal of Information Processing · doi:10.2197/ipsjjip.27.469 · Cited by 6
An empirical study on change recommendation
Mondal, M., Roy, C. K., & Schneider, K. A. (2015) · Computer Science and Software Engineering · Cited by 6
Personalized Code Recommendation
Nguyen, T. T., Vu, P., & Nguyen, T. T. (2019) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme.2019.00047 · Cited by 6
A text-based syntax completion method using LR parsing
Sasano, I., & Choi, K. (2020) · ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM) · doi:10.1145/3441296.3441395 · Cited by 6
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
Source Code Similarity and Clone Search
Keivanloo, I. (2013) · Spectrum Research Repository (Concordia University) · Cited by 6
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 6
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
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
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
Metadata
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
