Fetching the paper…
Reading the bibliography…
In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement.
Response time and display rate in human performance with computers
Ben Shneiderman. 1984 · 1984
Earlier work this paper cites.
Real-Time AI Systems: A Definition and An Architecture.. In IJCAI . Citeseer, 256–264
Rajendra T Dodhiawala, NS Sridharan, Peter Raulefs, and Cynthia Pickering. 1989 · 1989
Earlier work this paper cites.
Genetic Programming: On the Programming of Computers by Means of Natural Selection
J. R. Koza. 1992 · 1992
Earlier work this paper cites.
Design Patterns
Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. 1995 · 1995
Earlier work this paper cites.
Metrics for reuse of object–oriented software. In 4 t h 4^{th} Software Quality Management Conference . Cambridge
Maja Milankovic–Atkinson and Elli Georgiadou. 1996 · 1996
Earlier work this paper cites.
Search Based Software Engineering
Mark Harman and Bryan F. Jones. 2001 · 2001
Earlier work this paper cites.
Searching for Resource-Efficient Programs: Low-Power Pseudorandom Number Generators. In 2008 Genetic and Evolutionary Computation Conference (GECCO 2008) . Atlanta, USA, 1775–1782
David R. White, John Clark, Jeremy Jacob, and Simon Poulding. 2008 · 2008
Earlier work this paper cites.
Why Source Code Analysis and Manipulation Will Always Be Important. In 10 t h 10^{th} IEEE International Working Conference on Source Code Analysis and Manipulation . Timisoara, Romania, 7–19
Mark Harman. 2010 · 2010
Earlier work this paper cites.
A systematic literature survey of integration testing in component-based software engineering. In 2010 International Conference on Computer and Communication Technology (ICCCT) . IEEE, 562–568
S Phani Shashank, Praneeth Chakka, and D Vijay Kumar. 2010 · 2010
Earlier work this paper cites.
Genetic programming for shader simplification
Pitchaya Sitthi-amorn, Nicholas Modly, Westley Weimer, and Jason Lawrence. 2011 · 2011
Earlier work this paper cites.
Evolutionary Improvement of Programs
David Robert White, Andrea Arcuri, and John A. Clark. 2011 · 2011
Earlier work this paper cites.
Automated concolic testing of smartphone apps. In Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering . ACM, 59
Saswat Anand, Mayur Naik, Mary Jean Harrold, and Hongseok Yang. 2012 · 2012
Earlier work this paper cites.
The Model-View-Viewmodel (MVVM) design pattern
Chris Anderson. 2012 · 2012
Earlier work this paper cites.
Search Based Software Engineering: Trends, Techniques and Applications
Mark Harman, Afshin Mansouri, and Yuanyuan Zhang. 2012 · 2012
Earlier work this paper cites.
Dynamic Adaptive Search Based Software Engineering Needs Fast Approximate Metrics (Keynote Paper). In 4 t h 4^{th} International Workshop on Emerging Trends in Software Metrics (WeTSOM 2013) . San Francisco, USA
Mark Harman, John Clark, and Mel Ó Cinnédie. 2013 · 2013
Cited alongside, same era.
SEEDS: A Software Engineer’s Energy-optimization Decision Support Framework. In 36th International Conference on Software Engineering (Hyderabad, India). ACM, New York, NY, USA, 503–514
Irene Manotas, Lori Pollock, and James Clause. 2014 · 2014
Cited alongside, same era.
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
Mark Harman, Yue Jia, and Yuanyuan Zhang. 2015 · 2015
Cited alongside, same era.
Formal verification methods
Osman Hasan and Sofiene Tahar. 2015 · 2015
Cited alongside, same era.
Online learning: A comprehensive survey
Steven CH Hoi, Doyen Sahoo, Jing Lu, and Peilin Zhao. 2021 · 2021
Later among the works it cites.
Evaluating Automatic Program Repair Capabilities to Repair API Misuses
Maria Kechagia, Sergey Mechtaev, Federica Sarro, and Mark Harman. 2022 · 2021
Later among the works it cites.
Scaling Genetic Improvement and Automated Program Repair (keynote paper). In 3rd IEEE/ACM International Workshop on Automated Program Repair, APR@ICSE 2022, Pittsburgh, PA, USA, May 19, 2022 . IEEE, 1–7
Mark Harman. 2022 · 2022
Later among the works it cites.
Real-Time Communication
Hermann Kopetz and Wilfried Steiner. 2022 · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
William B. Langdon and Mark Harman. 2015 · 2015
Cited alongside, same era.
Deep Parameter Optimisation. In Genetic and evolutionary computation conference (GECCO 2015) . Madrid, Spain, 1375–1382
Fan Wu, Mark Harman, Yue Jia, Jens Krinke, and Westley Weimer. 2015 · 2015
Cited alongside, same era.
Are developers aware of the architectural impact of their changes?. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017, Urbana, IL, USA, October 30 - November 03, 2017 . 95–105
Matheus Paixão, Jens Krinke, DongGyun Han, Chaiyong Ragkhitwetsagul, and Mark Harman. 2017 · 2017
Cited alongside, same era.
Genetic Improvement of Software: a Comprehensive Survey
Justyna Petke, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, and John R. Woodward. 2018 · 2017
Cited alongside, same era.
Approximate Oracles and Synergy in Software Energy Search Spaces
Bobby R. Bruce, Justyna Petke, Mark Harman, and Earl T. Barr. 2019 · 2018
Cited alongside, same era.
Automated program repair
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019 · 2019
Cited alongside, same era.
SapFix: Automated End-to-End Repair at Scale. In International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) track . Montreal, Canada
Alexandru Marginean, Johannes Bader, Satish Chandra, Mark Harman, Yue Jia, Ke Mao, Alexander Mols, and Andrew Scott. 2019 · 2019
Cited alongside, same era.
Harnessing evolution for multi-hunk program repair. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 13–24
Seemanta Saha, Ripon K. Saha, and Mukul R. Prasad. 2019 · 2019
Cited alongside, same era.
Software Testing Research Challenges: An Industrial Perspective. In 2023 IEEE Conference on Software Testing, Verification and Validation (ICST 2023) . IEEE, 1–10
Nadia Alshahwan, Mark Harman, and Alexandru Marginean. 2023 · 2023
Later among the works it cites.
Large Language Models for Software Engineering: Survey and Open Problems. In ICSE Future of Software Engineering (FoSE 2023)
Angela Fan, Beliz Gokkaya, Mitya Lyubarskiy, Mark Harman, Shubho Sengupta, Shin Yoo, and Jie Zhang. 2023 · 2023
Later among the works it cites.
Baldur: Whole-proof generation and repair with large language models. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE 2023) . 1229–1241
Emily First, Markus Rabe, Talia Ringer, and Yuriy Brun. 2023 · 2023
Later among the works it cites.
Large Language Models for Software Engineering: A Systematic Literature Review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2023 · 2023
Later among the works it cites.
Software testing with large language model: Survey, landscape, and vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2023 · 2023
Later among the works it cites.
ReAct: Synergizing Reasoning and Acting in Language Models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023 · 2023
Later among the works it cites.
GitHub Copilot now has a better AI model and new capabilities
Shuyin Zhao. [n. d.] · 2023
Later among the works it cites.
Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad, Michael Bolin, Daniel Cheng, Negar Ghorbani, Renuka Fernandez, Nachiappan Nagappan, and Peter C Rigby. 2024 · 2024
Closest in time.