Fetching the paper…
Reading the bibliography…
This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE).
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 1877–1901
1901
Earlier work this paper cites.
A. M. Turing, “Checking a large routine,” in Report of a Conference on High Speed Automatic Calculating Machines . Cambridge, England: University Mathematical Laboratory, Jun. 1949, pp. 67–69
1949
Earlier work this paper cites.
Z. Manna and R. J. Waldinger, “Toward automatic program synthesis,” Communications of the ACM , vol. 14, no. 3, pp. 151–164, 1971
1971
Earlier work this paper cites.
J. Darlington and R. M. Burstall, “A system which automatically improves programs,” Acta Informatica , vol. 6, pp. 41–60, 1976
1976
Earlier work this paper cites.
T. J. McCabe, “A complexity measure,” vol. 2, pp. 308–320, 1976
1976
Earlier work this paper cites.
J. Darlington and R. M. Burstall, “A transformation system for developing recursive programs,” J. ACM , vol. 24, no. 1, pp. 44–67, 1977
1977
Earlier work this paper cites.
M. H. Halstead, Elements of Software Science . Elsevier, 1977
1977
Earlier work this paper cites.
H. Partsch, The CIP Transformation System , 1984, pp. 305–322, peter Pepper (ed.)
1984
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature , vol. 323, no. 6088, pp. 533–536, 1986
1986
Earlier work this paper cites.
A. V. Aho, R. Sethi, and J. D. Ullman, Compilers: Principles, techniques and tools , 1986
1986
Earlier work this paper cites.
M. Lehman and W. Turski, “Essential properties of ipses,” ACM SIGSOFT Software Engineering Notes , vol. 12, no. 1, pp. 52–55, 1987
1987
Earlier work this paper cites.
M. Ward, F. W. Calliss, and M. Munro, “The maintainer’s assistant,” in International Conference on Software Maintenance (ICSM 1989) , Los Alamitos, California, USA, 1989, p. 307
1989
Earlier work this paper cites.
T. J. Ostrand and E. J. Weyuker, “Data flow-based test adequacy analysis for languages with pointers,” in Symposium on Testing, Analysis, and Verification (TAV4) , Victoria, BC, Canada, 1991, pp. 74–86
1991
Earlier work this paper cites.
J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection . Cambridge, MA: MIT Press, 1992
1992
Earlier work this paper cites.
C. W. Krueger, “Software reuse,” ACM Computing Surveys (CSUR) , vol. 24, no. 2, pp. 131–183, 1992
1992
Earlier work this paper cites.
K. B. Gallagher, “Evaluating the surgeon’s assistant: Results of a pilot study,” in International Conference on Software Maintenance (ICSE ’92) , Los Alamitos, California, USA, Nov. 1992, pp. 236–244
1992
Earlier work this paper cites.
R. Pressman, Software Engineering: A Practitioner’s Approach , 3rd ed. Maidenhead, Berkshire, England, UK.: McGraw-Hill Book Company Europe, 1992, european adaptation (1994). Adapted by Darrel Ince. ISBN 0-07-707936-1
1994
Earlier work this paper cites.
E. Gamma, R. Helm, R. Johnson, and J. Vlissides, Design Patterns . Addison-Wesley, 1995
1995
Earlier work this paper cites.
C. Cornes, J. Courant, J.-C. Filliatre, G. Huet, P. Manoury, C. Munoz, C. Murthy, C. Paulin-Mohring, A. Saibi, and B. Werner, “The coq proof assistant, reference manual, version 5.10,” Inria, Institut National de Recherche en Informatique et en Automatique, Technical Report RT-0177, Jul. 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
M. Harman and B. F. Jones, “Search based software engineering,” Information and Software Technology , vol. 43, no. 14, pp. 833–839, Dec. 2001
2001
Earlier work this paper cites.
2001
Earlier work this paper cites.
C. J. Neill and P. A. Laplante, “Requirements engineering: the state of the practice,” IEEE software , vol. 20, no. 6, pp. 40–45, 2003
2003
Earlier work this paper cites.
G. Spanoudakis and A. Zisman, “Software traceability: a roadmap,” in Handbook of software engineering and knowledge engineering: vol 3: recent advances . World Scientific, 2005, pp. 395–428
2005
Earlier work this paper cites.
S. Easterbrook, “Empirical research methods for software engineering,” in Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’07. New York, NY, USA: Association for Computing Machinery, 2007, p. 574
2007
Earlier work this paper cites.
C. Pacheco and M. D. Ernst, “Randoop: feedback-directed random testing for java,” in Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion , 2007, pp. 815–816
2007
Earlier work this paper cites.
A. Bertolino, “Software testing research: Achievements, challenges, dreams,” in Future of Software Engineering (FOSE’07) . IEEE, 2007, pp. 85–103
2007
Earlier work this paper cites.
Y. Zhang, A. Finkelstein, and M. Harman, “Search based requirements optimisation: Existing work and challenges,” in International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ’08) , vol. 5025. Montpellier, France: Springer LNCS, 2008, pp. 88–94
2008
Earlier work this paper cites.
R. A. Santelices, P. K. Chittimalli, T. Apiwattanapong, A. Orso, and M. J. Harrold, “Test-suite augmentation for evolving software,” in 23 r d 23^{rd} Automated Software Engineering (ASE ’08) . L’Aquila, Italy: IEEE, 2008, pp. 218–227
2008
Earlier work this paper cites.
D. R. White, J. Clark, J. Jacob, and S. Poulding, “Searching for resource-efficient programs: Low-power pseudorandom number generators,” in 2008 Genetic and Evolutionary Computation Conference (GECCO 2008) , Atlanta, USA, Jul. 2008, pp. 1775–1782
2008
Earlier work this paper cites.
A. E. Hassan, “The road ahead for mining software repositories,” in 2008 Frontiers of Software Maintenance . IEEE, 2008, pp. 48–57
2008
Earlier work this paper cites.
M. H. Hamilton and W. R. Hackler, “Universal systems language: lessons learned from apollo,” Computer , vol. 41, no. 12, pp. 34–43, 2008
2008
Earlier work this paper cites.
M. Bruch, M. Monperrus, and M. Mezini, “Learning from examples to improve code completion systems,” in Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering , ser. ESEC/FSE ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 213–222
2009
Earlier work this paper cites.
J. H. Perkins, S. Kim, S. Larsen, S. P. Amarasinghe, J. Bachrach, M. Carbin, C. Pacheco, F. Sherwood, S. Sidiroglou, G. Sullivan, W.-F. Wong, Y. Zibin, M. D. Ernst, and M. C. Rinard, “Automatically patching errors in deployed software,” in Proceedings of the 22 n d 22^{nd} Symposium on Operating Systems Principles (SOSP’09), Operating Systems Review (OSR) , October 2009, pp. 87–102
2009
Earlier work this paper cites.
M. Gabel and Z. Su, “A study of the uniqueness of source code,” in 18 t h 18^{th} ACM SIGSOFT international symposium on foundations of software engineering (FSE 2010) . Santa Fe, New Mexico, USA: ACM, 7-11 Nov. 2010, pp. 147–156
2010
Earlier work this paper cites.
A. Arcuri and L. Briand, “A practical guide for using statistical tests to assess randomized algorithms in software engineering,” in 33 r d 33^{rd} International Conference on Software Engineering (ICSE’11) . New York, NY, USA: ACM, 2011, pp. 1–10
2011
Earlier work this paper cites.
Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” IEEE Transactions on Software Engineering , vol. 37, no. 5, pp. 649 – 678, September–October 2011
2011
Earlier work this paper cites.
M. Harman, A. Mansouri, and Y. Zhang, “Search based software engineering: Trends, techniques and applications,” ACM Computing Surveys , vol. 45, no. 1, pp. 11:1–11:61, November 2012
2012
Earlier work this paper cites.
M. Harman, P. McMinn, J. Souza, and S. Yoo, “Search based software engineering: Techniques, taxonomy, tutorial,” in Empirical software engineering and verification: LASER 2009-2010 , B. Meyer and M. Nordio, Eds. Springer, 2012, pp. 1–59, LNCS 7007
2012
Earlier work this paper cites.
A. Hindle, E. Barr, Z. Su, P. Devanbu, and M. Gabel, “On the naturalness of software,” in International Conference on Software Engineering (ICSE 2012) , Zurich, Switzerland, 2012
2012
Earlier work this paper cites.
S. Yoo and M. Harman, “Test data regeneration: Generating new test data from existing test data,” Journal of Software Testing, Verification and Reliability , vol. 22, no. 3, pp. 171–201, May 2012
2012
Earlier work this paper cites.
C. Le Goues, T. Nguyen, S. Forrest, and W. Weimer, “GenProg: A generic method for automatic software repair,” IEEE Transactions on Software Engineering , vol. 38, no. 1, pp. 54–72, 2012
2012
Earlier work this paper cites.
M. Harman, W. B. Langdon, Y. Jia, D. R. White, A. Arcuri, and J. A. Clark, “The GISMOE challenge: Constructing the Pareto program surface using genetic programming to find better programs (keynote paper),” in 27 t h 27^{th} IEEE/ACM International Conference on Automated Software Engineering (ASE 2012) , Essen, Germany, September 2012, pp. 1–14
2012
Earlier work this paper cites.
J. Cleland-Huang, O. Gotel, A. Zisman et al. , Software and systems traceability . Springer, 2012, vol. 2, no. 3
2012
Earlier work this paper cites.
S. Anand, A. Bertolino, E. Burke, T. Y. Chen, J. Clark, M. B. Cohen, W. Grieskamp, M. Harman, M. J. Harrold, J. Li, P. McMinn, and H. Zhu, “An orchestrated survey of methodologies for automated software test case generation,” Journal of Systems and Software , vol. 86, no. 8, pp. 1978–2001, August 2013
2013
Earlier work this paper cites.
C. Cadar and K. Sen, “Symbolic execution for software testing: Three decades later,” Communications of the ACM , vol. 56, no. 2, pp. 82–90, Feb. 2013
2013
Earlier work this paper cites.
T. Menzies and T. Zimmermann, “Software analytics: so what?” IEEE Software , vol. 30, no. 4, pp. 31–37, 2013
2013
Earlier work this paper cites.
E. T. Barr, Y. Brun, P. Devanbu, M. Harman, and F. Sarro, “The plastic surgery hypothesis,” in 22 n d 22^{nd} ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014) , Hong Kong, China, November 2014, pp. 306–317
2014
Earlier work this paper cites.
K. Androutsopoulos, D. Clark, H. Dan, M. Harman, and R. Hierons, “An analysis of the relationship between conditional entropy and failed error propagation in software testing,” in 36 t h 36^{th} International Conference on Software Engineering (ICSE 2014) , Hyderabad, India, June 2014, pp. 573–583
2014
Earlier work this paper cites.
M. Harman, Y. Jia, W. B. Langdon, J. Petke, I. H. Moghadam, S. Yoo, and F. Wu, “Genetic improvement for adaptive software engineering (keynote paper),” in 9 t h 9^{th} International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2014) . New York, NY, USA: ACM, 2014, pp. 1–4
2014
Earlier work this paper cites.
D. Li, A. H. Tran, and W. G. J. Halfond, “Making web applications more energy efficient for OLED smartphones,” in 36th International Conference on Software Engineering (ICSE 2014) . New York, NY, USA: ACM, 2014, pp. 527–538
2014
Earlier work this paper cites.
M. Harman, Y. Jia, and Y. Zhang, “Achievements, open problems and challenges for search based software testing (keynote paper),” in 8 t h 8^{th} IEEE International Conference on Software Testing, Verification and Validation (ICST 2015) , Graz, Austria, April 2015
2015
Earlier work this paper cites.
E. T. Barr, M. Harman, P. McMinn, M. Shahbaz, and S. Yoo, “The oracle problem in software testing: A survey,” IEEE Transactions on Software Engineering , vol. 41, no. 5, pp. 507–525, May 2015
2015
Earlier work this paper cites.
W. B. Langdon and M. Harman, “Optimising existing software with genetic programming,” IEEE Transactions on Evolutionary Computation (TEVC) , vol. 19, no. 1, pp. 118–135, Feb 2015
2015
Earlier work this paper cites.
F. Wu, M. Harman, Y. Jia, J. Krinke, and W. Weimer, “Deep parameter optimisation,” in Genetic and evolutionary computation conference (GECCO 2015) , Madrid, Spain, July 2015, pp. 1375–1382
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Gulwani, O. Polozov, R. Singh et al. , “Program synthesis,” Foundations and Trends in Programming Languages , vol. 4, no. 1-2, pp. 1–119, 2017
2017
Earlier work this paper cites.
T. T. Chekam, M. Papadakis, Y. L. Traon, and M. Harman, “An empirical study on mutation, statement and branch coverage fault revelation that avoids the unreliable clean program assumption,” in Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, May 20-28, 2017 , 2017, pp. 597–608
2017
Earlier work this paper cites.
T. Mariani and S. R. Vergilio, “A systematic review on search-based refactoring,” Information and Software Technology , vol. 83, pp. 14–34, 2017
2017
Earlier work this paper cites.
W. Martin, F. Sarro, Y. Jia, Y. Zhang, and M. Harman, “A survey of app store analysis for software engineering,” IEEE Transactions on Software Engineering , vol. 43, no. 9, 2017
2017
Earlier work this paper cites.
C. Lebeuf, M.-A. Storey, and A. Zagalsky, “Software bots,” IEEE Software , vol. 35, no. 1, pp. 18–23, 2017
2017
Earlier work this paper cites.
J. Petke, S. O. Haraldsson, M. Harman, W. B. Langdon, D. R. White, and J. R. Woodward, “Genetic improvement of software: a comprehensive survey,” IEEE Transactions on Evolutionary Computation , vol. 22, no. 3, pp. 415–432, Jun. 2018
2018
Earlier work this paper cites.
M. Harman and P. O’Hearn, “From start-ups to scale-ups: Opportunities and open problems for static and dynamic program analysis (keynote paper),” in 18 t h 18^{th} IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2018) , Madrid, Spain, September 23rd-24th 2018, pp. 1–23
2018
Earlier work this paper cites.
M. Monperrus, “Automatic software repair: a bibliography,” ACM Computing Surveys (CSUR) , vol. 51, no. 1, p. 17, 2018
2018
Earlier work this paper cites.
T. Menzies and T. Zimmermann, “Software analytics: What’s next?” IEEE Software , vol. 35, no. 5, pp. 64–70, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
C. L. Goues, M. Pradel, and A. Roychoudhury, “Automated program repair,” Communications of the ACM , vol. 62, no. 12, pp. 56–65, 2019
2019
Earlier work this paper cites.
M. Papadakis, M. Kintis, J. Zhang, Y. Jia, Y. L. Traon, and M. Harman, “Mutation testing advances: An analysis and survey,” Advances in Computers , vol. 112, pp. 275–378, 2019
2019
Earlier work this paper cites.
R. Abou Assi, C. Trad, M. Maalouf, and W. Masri, “Coincidental correctness in the defects4j benchmark,” Software Testing, Verification and Reliability , vol. 29, no. 3, p. e1696, 2019
2019
Earlier work this paper cites.
A. Marginean, J. Bader, S. Chandra, M. Harman, Y. Jia, K. Mao, A. Mols, and A. Scott, “SapFix: Automated end-to-end repair at scale,” in International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) track , Montreal, Canada, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
B. R. Bruce, J. Petke, M. Harman, and E. T. Barr, “Approximate oracles and synergy in software energy search spaces,” IEEE Transactions on Software Engineering , vol. 45, no. 11, pp. 1150–1169, 2019
2019
Earlier work this paper cites.
Z. Sun, J. M. Zhang, M. Harman, M. Papadakis, and L. Zhang, “Automatic testing and improvement of machine translation,” in ICSE ’20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020 , G. Rothermel and D. Bae, Eds. ACM, 2020, pp. 974–985. [Online]. Available: https://doi.org/10.1145/3377811.3380420
2020
Earlier work this paper cites.
P. Henderson, J. Hu, J. Romoff, E. Brunskill, D. Jurafsky, and J. Pineau, “Towards the systematic reporting of the energy and carbon footprints of machine learning,” J. Mach. Learn. Res. , vol. 21, no. 1, jan 2020
2020
Earlier work this paper cites.
A. Cantino, “Prompt engineering tips and tricks with GPT-3,” 2021. [Online]. Available: https://blog.andrewcantino.com/blog/2021/04/21/prompt-engineering-tips-and-tricks/
2021
Earlier work this paper cites.
Mark Chen et al., “Evaluating Large Language Models Trained on Code,” Jul. 2021, arXiv:2107.03374
2021
Earlier work this paper cites.
B. Berabi, J. He, V. Raychev, and M. T. Vechev, “TFix: Learning to fix coding errors with a text-to-text transformer,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event , ser. Proceedings of Machine Learning Research, M. Meila and T. Zhang, Eds., vol. 139. PMLR, 2021, pp. 780–791
2021
Earlier work this paper cites.
K. Bojarczuk, I. Dvortsova, J. George, N. Gucevska, M. Harman, M. Lomeli, S. Lucas, E. Meijer, R. Rojas, and S. Sapora, “Measurement challenges for cyber cyber digital twins: Experiences from the deployment of Facebook’s WW simulation system (keynote paper),” in ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM ’21) , October 2021, keynote talk given jointly by Maria Lomeli and Mark Harman
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
X. Luo, Y. Xue, Z. Xing, and J. Sun, “Prcbert: Prompt learning for requirement classification using bert-based pretrained language models,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–13
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
C. Bird, D. Ford, T. Zimmermann, N. Forsgren, E. Kalliamvakou, T. Lowdermilk, and I. Gazit, “Taking flight with copilot: Early insights and opportunities of ai-powered pair-programming tools,” Queue , vol. 20, no. 6, pp. 35–57, 2022
2022
Cited alongside, same era.
F. F. Xu, U. Alon, G. Neubig, and V. J. Hellendoorn, “A systematic evaluation of large language models of code,” in Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming . San Diego CA USA: ACM, Jun. 2022, pp. 1–10
2022
Cited alongside, same era.
E. Jiang, E. Toh, A. Molina, K. Olson, C. Kayacik, A. Donsbach, C. J. Cai, and M. Terry, “Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models,” in CHI Conference on Human Factors in Computing Systems . New Orleans LA USA: ACM, Apr. 2022, pp. 1–19
2022
Cited alongside, same era.
2022
Cited alongside, same era.
N. Jain, S. Vaidyanath, A. Iyer, N. Natarajan, S. Parthasarathy, S. Rajamani, and R. Sharma, “Jigsaw: large language models meet program synthesis,” in Proceedings of the 44th International Conference on Software Engineering . Pittsburgh Pennsylvania: ACM, May 2022, pp. 1219–1231
2022
Cited alongside, same era.
J. Sun, Q. V. Liao, M. Muller, M. Agarwal, S. Houde, K. Talamadupula, and J. D. Weisz, “Investigating Explainability of Generative AI for Code through Scenario-based Design,” in 27th International Conference on Intelligent User Interfaces . Helsinki Finland: ACM, Mar. 2022, pp. 212–228
2022
Cited alongside, same era.
2022
Cited alongside, same era.
N. Nguyen and S. Nadi, “An empirical evaluation of github copilot’s code suggestions,” in Proceedings of the 19th International Conference on Mining Software Repositories , ser. MSR ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 1–5
2022
Cited alongside, same era.
2023
Closest in time.
K. Liu, Y. Han, J. Zhang, Z. Chen, F. Sarro, M. Harman, G. Huang, and Y. Ma, “Who judges the judge: An empirical study on online judge tests,” in ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2023) , Jan. 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models,” 2023
2023
Closest in time.
J. Hu, Q. Zhang, and H. Yin, “Augmenting greybox fuzzing with generative ai,” 2023, arXiv:2306.06782
2023
Closest in time.
A. Moradi Dakhel, A. Nikanjam, V. Majdinasab, F. Khomh, and M. C. Desmarais, “Effective test generation using pre-trained large language models and mutation testing,” arXiv e-prints , pp. arXiv–2308, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
S. Kang, J. Yoon, and S. Yoo, “Large language models are few-shot testers: Exploring llm-based general bug reproduction,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 2312–2323
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. E. I. Brownlee, J. Callan, K. Even-Mendoza, A. Geiger, C. Hanna, J. Petke, F. Sarro, and D. Sobania, “Enhancing genetic improvement mutations using large language models,” in SSBSE 2023: Challenge Track . San Francisco, USA: Springer, 8 Dec 2023, to appear
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. Akli, G. Haben, S. Habchi, M. Papadakis, and Y. Le Traon, “FlakyCat: Predicting flaky tests categories using few-shot learning,” in 2023 IEEE/ACM International Conference on Automation of Software Test (AST) , 2023, pp. 140–151
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
H. Joshi, J. C. Sanchez, S. Gulwani, V. Le, G. Verbruggen, and I. Radiček, “Repair is nearly generation: Multilingual program repair with LLMs,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, 2023, pp. 5131–5140
2023
Closest in time.
2023
Closest in time.
E. Mashhadi, H. Ahmadvand, and H. Hemmati, “Method-level bug severity prediction using source code metrics and llms,” 2023
2023
Closest in time.
2023
Closest in time.
Y. Wei, C. S. Xia, and L. Zhang, “Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair,” in FSE 2023 , 2023
2023
Closest in time.
C. S. Xia and L. Zhang, “Conversational Automated Program Repair,” Jan. 2023, arXiv:2301.13246
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. S. Xia, Y. Ding, and L. Zhang, “Revisiting the plastic surgery hypothesis via large language models,” in ASE 2023 , 2023
2023
Closest in time.
2023
Closest in time.
D. Sobania, A. Geiger, J. Callan, A. Brownlee, C. Hanna, R. Moussa, M. Zamorano Lopez, J. Petke, and F. Sarro, “Evaluating explanations for software patches generated by large language models,” in SSBSE 2023: Challenge Track , ser. LNCS. San Francisco, USA: Springer, 8 Dec 2023, to appear
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. Cummins, V. Seeker, D. Grubisic, M. Elhoushi, Y. Liang, B. Roziere, J. Gehring, F. Gloeckle, K. Hazelwood, G. Synnaeve, and H. Leather, “Large language models for compiler optimization,” 2023
2023
Closest in time.
Q. Huang, J. Zhu, Z. Li, Z. Xing, C. Wang, and X. Xu, “PCR-Chain: Partial code reuse assisted by hierarchical chaining of prompts on frozen copilot,” in 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) , 2023, pp. 1–5
2023
Closest in time.
M. Zakeri-Nasrabadi, S. Parsa, M. Ramezani, C. Roy, and M. Ekhtiarzadeh, “A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges,” Journal of Systems and Software , p. 111796, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Feng, S. Vanam, M. Cherukupally, W. Zheng, M. Qiu, and H. Chen, “Investigating code generation performance of ChatGPT with crowdsourcing social data,” in 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) , 2023, pp. 876–885
2023
Closest in time.
2023
Closest in time.
S. I. Ross, F. Martinez, S. Houde, M. Muller, and J. D. Weisz, “The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development,” in Proceedings of the 28th International Conference on Intelligent User Interfaces . Sydney NSW Australia: ACM, Mar. 2023, pp. 491–514
2023
Closest in time.
2023
Closest in time.
W. Heaven, “ChatGPT is going to change education, not destroy it,” MIT Technology review , April 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
“A large-scale survey on the usability of ai programming assistants: Successes and challenges,” in 46 t h 46^{th} International Conference on Software Engineering (ICSE 2024) , April 2024, to appear
2024
Closest in time.
“Prompting is all you need: Automated Android bug replay with Large Language Models,” in 46 t h 46^{th} International Conference on Software Engineering (ICSE 2024) , April 2024, to appear
2024
Closest in time.
“Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning,” in 46 t h 46^{th} International Conference on Software Engineering (ICSE 2024) , April 2024, to appear
2024
Closest in time.
“TRACED: Execution-aware pre-training for source code,” in 46 t h 46^{th} International Conference on Software Engineering (ICSE 2024) , April 2024, to appear
2024
Closest in time.