Fetching the paper…
Reading the bibliography…
Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings.
J. Cohen, “A coefficient of agreement for nominal scales,” Educational and psychological measurement , vol. 20, no. 1, pp. 37–46, 1960
1960
Earlier work this paper cites.
J. M. Morse, M. Barrett, M. Mayan, K. Olson, and J. Spiers, “Verification strategies for establishing reliability and validity in qualitative research,” International journal of qualitative methods , vol. 1, no. 2, pp. 13–22, 2002
2002
Earlier work this paper cites.
M. Lombard, J. Snyder-Duch, and C. C. Bracken, “Content analysis in mass communication: Assessment and reporting of intercoder reliability,” Human communication research , vol. 28, no. 4, pp. 587–604, 2002
2002
Earlier work this paper cites.
L. Finlay, ““outing” the researcher: The provenance, process, and practice of reflexivity,” Qualitative health research , vol. 12, no. 4, pp. 531–545, 2002
2002
Earlier work this paper cites.
S. Easterbrook, J. Singer, M.-A. Storey, and D. Damian, “Selecting empirical methods for software engineering research,” Guide to advanced empirical software engineering , pp. 285–311, 2008
2008
Earlier work this paper cites.
G. Sridhara, E. Hill, D. Muppaneni, L. Pollock, and K. Vijay-Shanker, “Towards automatically generating summary comments for java methods,” in Proceedings of the 25th IEEE/ACM international conference on Automated software engineering , 2010, pp. 43–52
2010
Earlier work this paper cites.
K. Krippendorff, Content analysis: An introduction to its methodology . Sage publications, 2018
2018
Earlier work this paper cites.
V. J. Hellendoorn, C. Bird, E. T. Barr, and M. Allamanis, “Deep learning type inference,” in Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT , 2018, pp. 152–162. [Online]. Available: https://doi.org/10.1145/3236024.3236051
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. LeClair, S. Jiang, and C. McMillan, “A neural model for generating natural language summaries of program subroutines,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 795–806
2019
Earlier work this paper cites.
M. Kamp, P. Kreutzer, and M. Philippsen, “Sesame: A data set of semantically similar java methods,” in 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR) . IEEE, 2019, pp. 529–533
2019
Earlier work this paper cites.
Z. Chen, S. Kommrusch, M. Tufano, L. Pouchet, D. Poshyvanyk, and M. Monperrus, “SequenceR: Sequence-to-sequence learning for end-to-end program repair,” IEEE Trans. Software Eng. , vol. 47, no. 9, pp. 1943–1959, 2021. [Online]. Available: https://doi.org/10.1109/TSE.2019.2940179
2019
Earlier work this paper cites.
R. S. Malik, J. Patra, and M. Pradel, “NL2Type: Inferring JavaScript function types from natural language information,” in Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019 , 2019, pp. 304–315. [Online]. Available: https://doi.org/10.1109/ICSE.2019.00045
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
M. Pradel, G. Gousios, J. Liu, and S. Chandra, “Typewriter: Neural type prediction with search-based validation,” in ESEC/FSE ’20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA, November 8-13, 2020 , 2020, pp. 209–220. [Online]. Available: https://doi.org/10.1145/3368089.3409715
2020
Earlier work this paper cites.
M. Allamanis, E. T. Barr, S. Ducousso, and Z. Gao, “Typilus: neural type hints,” in Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, PLDI , 2020, pp. 91–105. [Online]. Available: https://doi.org/10.1145/3385412.3385997
2020
Earlier work this paper cites.
D. Roy, S. Fakhoury, and V. Arnaoudova, “Reassessing automatic evaluation metrics for code summarization tasks,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 1105–1116
2021
Earlier work this paper cites.
J. Fischbach, J. Frattini, A. Spaans, M. Kummeth, A. Vogelsang, D. Mendez, and M. Unterkalmsteiner, “Automatic detection of causality in requirement artifacts: the cira approach,” in Requirements Engineering: Foundation for Software Quality: 27th International Working Conference, REFSQ 2021, Essen, Germany, April 12–15, 2021, Proceedings 27 . Springer, 2021, pp. 19–36
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
C. Zhang, J. Wang, Q. Zhou, T. Xu, K. Tang, H. Gui, and F. Liu, “A survey of automatic source code summarization,” Symmetry , vol. 14, no. 3, p. 471, 2022
2022
Earlier work this paper cites.
H. J. Kang, K. L. Aw, and D. Lo, “Detecting false alarms from automatic static analysis tools: How far are we?” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 698–709
2022
Earlier work this paper cites.
S. Haque, Z. Eberhart, A. Bansal, and C. McMillan, “Semantic similarity metrics for evaluating source code summarization,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 36–47
2022
Cited alongside, same era.
T. Ahmed and P. Devanbu, “Few-shot training llms for project-specific code-summarization,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–5
2022
Cited alongside, same era.
J. Patra and M. Pradel, “Nalin: learning from runtime behavior to find name-value inconsistencies in jupyter notebooks,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1469–1481
2022
Cited alongside, same era.
X. Hu, X. Xia, D. Lo, Z. Wan, Q. Chen, and T. Zimmermann, “Practitioners’ expectations on automated code comment generation,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1693–1705
2022
2024
Closest in time.
2024
Closest in time.
O. Dunay, D. Cheng, A. Tait, P. Thakkar, P. C. Rigby, A. Chiu, I. Ahmad, A. Ganesan, C. Maddila, V. Murali et al. , “Multi-line ai-assisted code authoring,” in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering , 2024, pp. 150–160
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
M. Pradel and S. Chandra, “Neural software analysis,” Commun. ACM , vol. 65, no. 1, pp. 86–96, 2022. [Online]. Available: https://doi.org/10.1145/3460348
2022
Cited alongside, same era.
2023
Cited alongside, same era.
G. V. Aher, R. I. Arriaga, and A. T. Kalai, “Using large language models to simulate multiple humans and replicate human subject studies,” in International Conference on Machine Learning . PMLR, 2023, pp. 337–371
2023
Cited alongside, same era.
A. Goel, A. Gueta, O. Gilon, C. Liu, S. Erell, L. H. Nguyen, X. Hao, B. Jaber, S. Reddy, R. Kartha et al. , “Llms accelerate annotation for medical information extraction,” in Machine Learning for Health (ML4H) . PMLR, 2023, pp. 82–100
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Closest in time.
G. Ryan, S. Jain, M. Shang, S. Wang, X. Ma, M. K. Ramanathan, and B. Ray, “Code-aware prompting: A study of coverage guided test generation in regression setting using llm,” in FSE , 2024
2024
Closest in time.
J. A. Pizzorno and E. D. Berger, “Coverup: Coverage-guided llm-based test generation,” 2024
2024
Closest in time.
C. S. Xia, M. Paltenghi, J. L. Tian, M. Pradel, and L. Zhang, “Fuzz4all: Universal fuzzing with large language models,” in ICSE , 2024
2024
Closest in time.
H. Ye and M. Monperrus, “Iter: Iterative neural repair for multi-location patches,” in ICSE , 2024
2024
Closest in time.
A. Silva, S. Fang, and M. Monperrus, “Repairllama: Efficient representations and fine-tuned adapters for program repair,” 2024
2024
Closest in time.
S. B. Hossain, N. Jiang, Q. Zhou, X. Li, W.-H. Chiang, Y. Lyu, H. Nguyen, and O. Tripp, “A deep dive into large language models for automated bug localization and repair,” in FSE , 2024
2024
Closest in time.
I. Bouzenia, P. Devanbu, and M. Pradel, “RepairAgent: An autonomous, LLM-based agent for program repair,” Preprint, 2024
2024
Closest in time.
J. Yang, C. E. Jimenez, K. Lieret, S. Yao, A. Wettig, K. Narasimhan, and O. Press, “Swe-agent: Agent-computer interfaces enable automated software engineering,” 2024
2024
Closest in time.
Y. Zhang, H. Ruan, Z. Fan, and A. Roychoudhury, “Autocoderover: Autonomous program improvement,” 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Z. He, C. Huang, C. C. Ding, S. Rohatgi, and T. K. Huang, “If in a crowdsourced data annotation pipeline, a GPT-4,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI 2024, Honolulu, HI, USA, May 11-16, 2024 , F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. O. T. Dugas, and I. Shklovski, Eds. ACM, 2024, pp. 1040:1–1040:25. [Online]. Available: https://doi.org/10.1145/3613904.3642834
2024
Closest in time.
2024
Closest in time.
H. Kim, K. Mitra, R. L. Chen, S. Rahman, and D. Zhang, “Meganno+: A human-llm collaborative annotation system,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - System Demonstrations, St. Julians, Malta, March 17-22, 2024 , N. Aletras and O. D. Clercq, Eds. Association for Computational Linguistics, 2024, pp. 168–176. [Online]. Available: https://aclanthology.org/2024.eacl-demo.18
2024
Closest in time.
X. Wang, H. Kim, S. Rahman, K. Mitra, and Z. Miao, “Human-llm collaborative annotation through effective verification of LLM labels,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI 2024, Honolulu, HI, USA, May 11-16, 2024 , F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. O. T. Dugas, and I. Shklovski, Eds. ACM, 2024, pp. 303:1–303:21. [Online]. Available: https://doi.org/10.1145/3613904.3641960
2024
Closest in time.
2024
Closest in time.
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing et al. , “Judging llm-as-a-judge with mt-bench and chatbot arena,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.