Improving code completion with program history
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Other effect size measures such as the common language effect size or A12 may slightly alter the discussion.
“Other effect size measures such as the common language effect size [44] or A 12 [45] may slightly alter the discussion.”
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Cites
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1994) · Psychological Review · doi:10.1037/0033-295x.101.2.343 · Cited by 17,469 · see citing works
Version models for software configuration management
Conradi, R., & Westfechtel, B. (1998) · ACM Computing Surveys · doi:10.1145/280277.280280 · Cited by 587 · see citing works
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 · see citing works
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 · see citing works
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 383 · see citing works
Reiss, S. P. (2009) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2009.5070525 · Cited by 309 · see citing works
Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise
Arisholm, E., Gallis, H., Dybå, T., & Sjøberg, D. I. K. (2007) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2007.17 · Cited by 274 · see citing works
Bajracharya, S. K., Ngo, T. C., Linstead, E. J., Dou, Y., Rigor, P., Baldi, P., & Lopes, C. V. (2006) · Companion to the ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA Companion) · doi:10.1145/1176617.1176671 · Cited by 220 · see citing works
Using benchmarking to advance research: a challenge to software engineering
Sim, S. E., Easterbrook, S., & Holt, R. C. (2003) · doi:10.5555/776816.776826 · Cited by 201 · see citing works
Using benchmarking to advance research: a challenge to software engineering
Sim, S. E., Easterbrook, S., & Holt, R. C. (2003) · Proceedings/Proceedings - International Conference on Software Engineering · doi:10.1109/icse.2003.1201189 · Cited by 150 · see citing works
Little, G., & Miller, R. C. (2007) · doi:10.1145/1321631.1321646 · Cited by 96 · see citing works
Robbes, R., & Lanza, M. (2008) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/1368088.1368219 · Cited by 81 · see citing works
Self-citationThe camel has two humps (working title)
Dehnadi, S., & Bornat, R. (2006) · Cited by 81 · see citing works
On the difficulty of replicating human subjects studies in software engineering
Lung, J., Aranda, J., Easterbrook, S., & Wilson, G. (2008) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/1368088.1368115 · Cited by 68 · see citing works
Mining a Change-Based Software Repository
Robbes, R. (2007) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2007.18 · Cited by 58 · see citing works
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 · see citing works
Self-citationLittle, G., & Miller, R. C. (2008) · Automated Software Engineering · doi:10.1007/s10515-008-0041-9 · Cited by 48 · see citing works
On using a benchmark to evaluate C++ extractors
Sim, S. E., Holt, R. C., & Easterbrook, S. (2003) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/wpc.2002.1021331 · Cited by 45 · see citing works
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 · see citing works
Self-citation
Flagged first, then by citations
1 of the 20 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
On the naturalness of software
Hindle, A., Barr, E. T., Su, Z., Gabel, M., & Dévanbu, P. (2012) · doi:10.5555/2337223.2337322 · Cited by 574 · see citing works
On the naturalness of software
Hindle, A., Barr, E. T., Su, Z., Gabel, M., & Devanbu, P. (2012) · 2012 34th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2012.6227135 · Cited by 425 · see citing works
On the naturalness of software
Hindle, A., Barr, E. T., Gabel, M., Su, Z., & Devanbu, P. (2016) · Communications of the ACM · doi:10.1145/2902362 · Cited by 307 · see citing works
Tu, Z., Su, Z., & Devanbu, P. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635875 · Cited by 238 · see citing works
On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot
Mastropaolo, A., Pascarella, L., Guglielmi, E., Ciniselli, M., Scalabrino, S., Oliveto, R., & Bavota, G. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00181 · Cited by 149 · see citing works
Automated library recommendation
Thung, F., Lo, D., & Lawall, J. (2013) · Working Conference on Reverse Engineering (WCRE) · doi:10.1109/wcre.2013.6671293 · Cited by 140 · see citing works
Solving the Search for Source Code
Stolee, K. T., Elbaum, S. G., & Dobos, D. (2014) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/2581377 · Cited by 122 · see citing works
Automatic parameter recommendation for practical API usage
Zhang, C., Yang, J., Zhang, Y., Fan, J., Zhang, X., Zhao, J., & Ou, P. (2012) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2012.6227136 · Cited by 105 · see citing works
An Empirical Study on the Usage of Transformer Models for Code Completion
Ciniselli, M., Cooper, N., Pascarella, L., Mastropaolo, A., Aghajani, E., Poshyvanyk, D., Penta, M. D., & Bavota, G. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3128234 · Cited by 101 · see citing works
An Empirical Study on the Usage of BERT Models for Code Completion
Ciniselli, M., Cooper, N., Pascarella, L., Poshyvanyk, D., Penta, M. D., & Bavota, G. (2021) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr52588.2021.00024 · Cited by 93 · see citing works
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 · see citing works
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 · see citing works
“…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].”
Learning Deep Semantics for Test Completion
Nie, P., Banerjee, R., Li, J. J., Mooney, R. J., & Gligoric, M. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00178 · Cited by 65 · see citing works
Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?
Peng, Y., Li, S., Gu, W., Li, Y., Wang, W., Gao, C., & Lyu, M. R.-T. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3197063 · Cited by 52 · see citing works
On the use of developers’ context for automatic refactoring of software anti-patterns
Morales, R., Soh, Z., Khomh, F., Antoniol, G., & Chicano, F. (2016) · Journal of Systems and Software · doi:10.1016/j.jss.2016.05.042 · Cited by 49 · see citing works
Liu, J., & Li, S. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241240460 · Cited by 40 · see citing works
Collecting and Processing Interaction Data for Recommendation Systems
Maalej, W., Fritz, T., & Robbes, R. (2013) · Springer eBooks · doi:10.1007/978-3-642-45135-5_7 · Cited by 37 · see citing works
Self-citationCACHECA: A Cache Language Model Based Code Suggestion Tool
Franks, C., Tu, Z., Dévanbu, P., & Hellendoorn, V. J. (2015) · Cited by 35 · see citing works
WebAPIRec: Recommending Web APIs to Software Projects via Personalized Ranking
Thung, F., Oentaryo, R., Lo, D., & Tian, Y. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2699222 · Cited by 32 · see citing works
A context model for IDE-based recommendation systems
Gašparič, M., Murphy, G. C., & Ricci⋆, F. (2016) · Journal of Systems and Software · doi:10.1016/j.jss.2016.09.012 · Cited by 32 · see citing works
API recommendation system for software development
Thung, F. (2016) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/2970276.2975940 · Cited by 31 · see citing works
An empirical study of work fragmentation in software evolution tasks
Sanchez, H., Robbes, R., & González, V. M. (2015) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2015.7081835 · Cited by 31 · see citing works
Self-citationCACHECA: A Cache Language Model Based Code Suggestion Tool
Franks, C., Tu, Z., Devanbu, P., & Hellendoorn, V. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.228 · Cited by 30 · see citing works
WebAPIRec: Recommending web APIs to software projects via personalized ranking
Thung, F., Oentaryo, R. J., Lo, D., & Tian, Y. (2017) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 29 · see citing works
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words
Liu, C., Xia, X., Lo, D., Liu, Z., Hassan, A. E., & Li, S. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3465403 · Cited by 42 · see citing works
Yu, H., Xia, X., Zhao, X., & Qiu, W. (2017) · Asia-Pacific Symposium on Internetware · doi:10.1145/3131704.3131721 · Cited by 27 · see citing works
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 · see citing works
Ciniselli, M., Pascarella, L., & Bavota, G. (2022) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3524842.3528440 · Cited by 26 · see citing works
Petersen, P., Hanenberg, S., & Robbes, R. (2014) · International Conference on Program Comprehension · doi:10.1145/2597008.2597152 · Cited by 26 · see citing works
Self-citationExploring the use of automated API migrating techniques in practice
Lamothe, M., & Shang, W. (2018) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3196398.3196420 · Cited by 25 · see citing works
Fuzzy Fine-Grained Code-History Analysis
Servant, F., & Jones, J. A. (2017) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2017.74 · Cited by 24 · see citing works
Non-Autoregressive Line-Level Code Completion
Liu, F., Fu, Z., Li, G., Jin, Z., Liu, H., Hao, Y., & Zhang, L. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3649594 · Cited by 22 · see citing works
The hidden cost of code completion
Jin, X., & Servant, F. (2018) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3196398.3196474 · Cited by 22 · see citing works
Wen, F., Aghajani, E., Nagy, C., Lanza, M., & Bavota, G. (2021) · International Conference on Software Engineering · doi:10.1109/icse43902.2021.00025 · Cited by 20 · see citing works
Self-citationBig Code Search: A Bibliography
Kim, K., Ghatpande, S., Kim, D., Zhou, X., Liu, K., Bissyandé, T. F., Klein, J., & Traon, Y. L. (2023) · ACM Computing Surveys · doi:10.1145/3604905 · Cited by 17 · see citing works
Fischer, L., & Hanenberg, S. (2015) · Symposium on Dynamic Languages (DLS) · doi:10.1145/2816707.2816720 · Cited by 16 · see citing works
Changes as First-Class Citizens
Soetens, Q. D., Robbes, R., & Demeyer, S. (2017) · ACM Computing Surveys · doi:10.1145/3038926 · Cited by 16 · see citing works
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 15 · see citing works
Automated variable renaming: are we there yet?
Mastropaolo, A., Aghajani, E., Pascarella, L., & Bavota, G. (2023) · Empirical Software Engineering · doi:10.1007/s10664-022-10274-8 · Cited by 15 · see citing works
Temporal code completion and navigation
Lee, Y. Y., Harwell, S., Sarfraz Khurshid, D., & Marinov, D. (2013) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2013.6606673 · Cited by 13 · see citing works
Assessing and Analyzing the Correctness of GitHub Copilot's Code Suggestions
Mo, R., Wang, D., Zhan, W., Jiang, Y., Wang, Y., Zhao, Y., Li, Z., & Ma, Y. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715108 · Cited by 11 · see citing works
SimIMA: a virtual Simulink intelligent modeling assistant
Adhikari, B., Rapos, E. J., & Stephan, M. (2023) · Software & Systems Modeling · doi:10.1007/s10270-023-01093-6 · Cited by 11 · see citing works
Temporal code completion and navigation
Lee, Y. Y., Harwell, S., Sarfraz Khurshid, D., & Marinov, D. (2013) · doi:10.5555/2486788.2486956 · Cited by 11 · see citing works
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 · see citing works
Developer Profiles for Recommendation Systems
Ying, A. T. T., & Robillard, M. P. (2013) · Springer eBooks · doi:10.1007/978-3-642-45135-5_8 · Cited by 11 · see citing works
Mining unit tests for code recommendation
Ghafari, M., Ghezzi, C., Mocci, A., & Tamburrelli, G. (2014) · International Conference on Program Comprehension · doi:10.1145/2597008.2597789 · Cited by 10 · see citing works
A study on repetitiveness of code completion operations
Omori, T., Kuwabara, H., & Maruyama, K. (2012) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2012.6405327 · Cited by 10 · see citing works
Fischer, L., & Hanenberg, S. (2015) · ACM SIGPLAN Notices · doi:10.1145/2936313.2816720 · Cited by 9 · see citing works
Noise in Mylyn interaction traces and its impact on developers and recommendation systems
Soh, Z., Khomh, F., Guéhéneuc, Y., & Antoniol, G. (2017) · Empirical Software Engineering · doi:10.1007/s10664-017-9529-x · Cited by 8 · see citing works
Zhao, X., Li, S., Yu, H., Wang, Y., & Qiu, W. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edp7227 · Cited by 8 · see citing works
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Heinemann, L. (2012) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 8 · see citing works
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Wang, S.-W., Wen, M., Lin, B., Liu, Y.-P., Bissyandé, T. F., & Mao, X.-G. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3597203 · Cited by 9 · see citing works
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Bauer, V., Volke, T., & Jürgens, E. (2014) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme.2014.80 · Cited by 7 · see citing works
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Simplifying Deep-Learning-Based Model for Code Search.Preprint
Liu, C., Xia, X., Lo, D., Liu, Z., Hassan, A. E., & Li, S. (2020) · arXiv (Cornell University) · Cited by 7 · see citing works
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Cruz, L. C., Sanchez, H., González, V. M., & Robbes, R. (2017) · Journal of Software: Evolution and Process · doi:10.1002/smr.1839 · Cited by 6 · see citing works
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Yamamoto, T., Yoshida, N., & Higo, Y. (2013) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec.2013.108 · Cited by 4 · see citing works
Development iterations based on web augmentation and context tasks
Marticorena, L. G., Morales, L. A., Antonelli, L., Rossi, G. H., & Firmenich, D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13694-2 · Cited by 4 · see citing works
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Omori, T., Kuwabara, H., & Maruyama, K. (2015) · Journal of information processing · doi:10.11185/imt.10.210 · Cited by 4 · see citing works
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Bruch, M. (2012) · TUbilio (Technical University of Darmstadt) · doi:10.26083/tuprints-00003137 · Cited by 4 · see citing works
PIONEER: improving the robustness of student models when compressing pre-trained models of code
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Wang, Z., Liu, F., Hao, Y., & Jin, Z. G. (2023) · Automated Software Engineering · doi:10.1007/s10515-023-00376-y · Cited by 3 · see citing works
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Lee, Y. Y., Marinov, D., & Johnson, R. E. (2015) · doi:10.5555/2818754.2818783 · Cited by 3 · see citing works
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Wang, C., Yang, Y., Liu, H., & Kang, L. (2019) · doi:10.1109/maint.2019.8666939 · Cited by 3 · see citing works
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Bunkerd, T., Wang, D., Kula, R. G., Ragkhitwetsagul, C., Choetkiertikul, M., Sunetnanta, T. T., Ishio, T., & Matsumoto, K. (2019) · International Workshop on Empirical Software Engineering in Practice (IWESEP) · doi:10.1109/iwesep49350.2019.00010 · Cited by 3 · see citing works
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Wan, Z., Murphy, G. C., & Xia, X. (2020) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3324884.3416544 · Cited by 3 · see citing works
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Matveev, A. O., Bystrov, A. V., Bibaev, V. I., & Povarov, N. I. (2020) · Vestnik NSU Series Information Technologies · doi:10.25205/1818-7900-2020-18-2-62-75 · Cited by 1 · see citing works
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Zheng, X., Wan, Z., Liu, S., Yang, K., Lo, D., & Xiao-hu, Y. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3578390 · Cited by 1 · see citing works
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Ciborowska, A., & Damevski, K. (2020) · IEEE Access · doi:10.1109/access.2020.3040156 · Cited by 1 · see citing works
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Wen, F., Ferrari, V., Aghajani, E., Nagy, C., Lanza, M., & Bavota, G. (2021) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme52107.2021.00062 · Cited by 1 · see citing works
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Gu, Z. (2013) · Cited by 1 · see citing works
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Asaduzzaman, M. (2018) · Cited by 1 · see citing works
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Lee, Y. Y. (2014) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Addressing scalability in API method call analytics
Çergani, E., Proksch, S., Nadi, S., & Mezini, M. (2016) · doi:10.1145/2989238.2989240
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- Authors
- Robbes, R., & Lanza, M.
- Affiliations
- University of Chile (CL)
- Università della Svizzera italiana (CH)
- Corresponding author
- Robbes, Romain
- Venue
- Automated Software Engineering
- Published
- 2010-02-12
- Volume
- 17
- Issue
- 2
- Pages
- 181-212
- ISSN
- 0928-8910 (ISSN-L)
- 1573-7535
- OpenAlex
- W2059556545
- Cited by
- 95
- References
- 20
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 8 · 2023: 10 · 2022: 5 · 2021: 6 · 2020: 4 · 2019: 3 · 2018: 5 · 2017: 8 · 2016: 6 · 2015: 10 · 2014: 6 · 2013: 7 · 2012: 7
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Automated Software Engineering (publishedVersion)
- Universidad de Chile (submittedVersion)
- Open Research (University of Surrey) (submittedVersion)
- 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, completion algorithm, benchmarking, developer productivity
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