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Empirical software engineering research on production systems has brought forth a better understanding of the software engineering process for practitioners and researchers alike.
Language Models Are Few-shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
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Understanding the Impact of Assumptions on Experimental Validity. In International Symposium on Empirical Software Engineering (ISESE) . 251–260
J. Carver, J. VanVoorhis, and V. Basili. 2004 · 2004
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Five misunderstandings about case-study research
Bent Flyvbjerg. 2006 · 2006
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Change Distilling: Tree Differencing for Fine-grained Source Code Change Extraction
Beat Fluri, Michael Wursch, Martin PInzger, and Harald Gall. 2007 · 2007
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Personal Opinion Surveys
Barbara A. Kitchenham and Shari Lawrence Pfleeger. 2008 · 2008
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Does Distributed Development Affect Software Quality? An Empirical Case Study of Windows Vista
Christian Bird, Nachiappan Nagappan, Premkumar Devanbu, Harald Gall, and Brendan Murphy. 2009 · 2009
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Characterizing and Predicting which Bugs Get Fixed: An Empirical Study of Microsoft Windows. In ACM/IEEE International Conference on Software Engineering (ICSE) . 495–504
Philip J Guo, Thomas Zimmermann, Nachiappan Nagappan, and Brendan Murphy. 2010 · 2010
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Do Time of Day and Developer Experience Affect Commit Bugginess?. In Working Conference on Mining Software Repositories (MSR) . 153–162
Jon Eyolfson, Lin Tan, and Patrick Lam. 2011 · 2011
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An Empirical Investigation into the Role of API-level Refactorings during Software Evolution. In IEEE/ACM International Conference on Software Engineering (ICSE) . 151–160
Miryung Kim, Dongxiang Cai, and Sunghun Kim. 2011 · 2011
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Confusing Claims for Data: A Critique of Common Practices for Presenting Qualitative Research on Learning
David Hammer and Leema K Berland. 2014 · 2013
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Analyze This! 145 Questions for Data Scientists in Software Engineering. In IEEE/ACM International Conference on Software Engineering (ICSE) . 12–23
Andrew Begel and Thomas Zimmermann. 2014 · 2014
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Sentiment Analysis of Commit Comments in GitHub: An Empirical Study. In Working Conference on Mining Software Repositories (MSR) . 352–355
Emitza Guzman, David Azócar, and Yang Li. 2014 · 2014
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Security and Emotion: Sentiment Analysis of Security Discussions on GitHub. In Working Conference on Mining Software Repositories (MSR) . 348–351
Daniel Pletea, Bogdan Vasilescu, and Alexander Serebrenik. 2014 · 2014
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A Practical Guide to Controlled Experiments of Software Engineering Tools with Human Participants
Amy J Ko, Thomas D LaToza, and Margaret M Burnett. 2015 · 2015
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How Practitioners Perceive the Relevance of Software Engineering Research. In ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) . 415–425
David Lo, Nachiappan Nagappan, and Thomas Zimmermann. 2015 · 2015
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A Cross-tool Communication Study on Program Analysis Tool nNtifications. In ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE) . 73–84
Brittany Johnson, Rahul Pandita, Justin Smith, Denae Ford, Sarah Elder, Emerson Murphy-Hill, Sarah Heckman, and Caitlin Sadowski. 2016 · 2016
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Performance Issues and Optimizations in JavaScript: An Empirical Study. In IEEE/ACM International Conference on Software Engineering (ICSE) . 61–72
Marija Selakovic and Michael Pradel. 2016 · 2016
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The Emerging Role of Data Scientists on Software Development Teams. In IEEE/ACM International Conference on Software Engineering (ICSE) . 96–107
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2016 · 2017
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Data Scientists in Software Teams: State of the Art and Challenges
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2017 · 2017
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Towards a Theory of Software Development Expertise. In ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) . 187–200
Sebastian Baltes and Stephan Diehl. 2018 · 2018
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Lessons from Building Static Analysis Tools at Google
Caitlin Sadowski, Edward Aftandilian, Alex Eagle, Liam Miller-Cushon, and Ciera Jaspan. 2018 · 2018
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Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. In Conference on Empirical Methods in Natural Language Processing (EMNLP) . 3911–3921
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018 · 2018
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Scaling Static Analyses at Facebook
Dino Distefano, Manuel Fähndrich, Francesco Logozzo, and Peter W O’Hearn. 2019 · 2019
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Tea: A High-level Language and Runtime System for Automating Statistical Analysis. In ACM Symposium on User Interface Software and Technology (UIST) . 591–603
Eunice Jun, Maureen Daum, Jared Roesch, Sarah Chasins, Emery Berger, Rene Just, and Katharina Reinecke. 2019 · 2019
Cited alongside, same era.
Threats of a Replication Crisis in Empirical Computer Science
Andy Cockburn, Pierre Dragicevic, Lonni Besançon, and Carl Gutwin. 2020 · 2020
Cited alongside, same era.
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks. In Annual Meeting of the Association for Computational Linguistics (ACL) . 8342–8360
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
Norbert: Transfer Learning for Requirements Classification. In IEEE International Requirements Engineering Conference (RE) . IEEE, 169–179
Tobias Hey, Jan Keim, Anne Koziolek, and Walter F Tichy. 2020 · 2020
Cited alongside, same era.
A Systematic Evaluation of Large Language Models of Code. In ACM SIGPLAN International Symposium on Machine Programming (MAPS) . 1–10
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
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Using Pre-trained Language Models to Resolve Textual and Semantic Merge Conflicts (Experience Paper). In ACM International Symposium on Software Testing and Analysis (ISSTA) . 77–88
Jialu Zhang, Todd Mytkowicz, Mike Kaufman, Ruzica Piskac, and Shuvendu K Lahiri. 2022 · 2022
Later among the works it cites.
ChatGPT Plugins
2023 · 2023
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Standards | Empirical Standards
2023 · 2023
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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Questions for Data Scientists in Software Engineering: A Replication. In ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) . 568–579
Hennie Huijgens, Ayushi Rastogi, Ernst Mulders, Georgios Gousios, and Arie van Deursen. 2020 · 2020
Cited alongside, same era.
HCI Guidelines for Gender Equity and Inclusivity
Morgan Klaus Scheuerman, Katta Spiel, Oliver L Haimson, Foad Hamidi, and Stacy M Branham. 2020 · 2020
Cited alongside, same era.
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Glinda: Supporting Data Science with Live Programming, GUIs and a Domain-specific Language. In ACM CHI Conference on Human Factors in Computing Systems . 1–11
Robert A DeLine. 2021 · 2021
Cited alongside, same era.
A Tale of Two Cities: Software Developers Working from Home during the Covid-19 Pandemic
Denae Ford, Margaret-Anne Storey, Thomas Zimmermann, Christian Bird, Sonia Jaffe, Chandra Maddila, Jenna L Butler, Brian Houck, and Nachiappan Nagappan. 2021 · 2021
Cited alongside, same era.
On the Introduction of Automatic Program Repair in Bloomberg
Serkan Kirbas, Etienne Windels, Olayori McBello, Kevin Kells, Matthew Pagano, Rafal Szalanski, Vesna Nowack, Emily Rowan Winter, Steve Counsell, David Bowes, et al · 2021
Cited alongside, same era.
On Implicit Assumptions Underlying Software Engineering Research. In International Conference on Evaluation and Assessment in Software Engineering (EASE) . 336–339
Lutz Prechelt. 2021 · 2021
Cited alongside, same era.
Zero-shotTtext-to-image Generation. In International Conference on Machine Learning (ICML) . PMLR, 8821–8831
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge
Sören Auer, Dante AC Barone, Cassiano Bartz, Eduardo G Cortes, Mohamad Yaser Jaradeh, Oliver Karras, Manolis Koubarakis, Dmitry Mouromtsev, Dmitrii Pliukhin, Daniil Radyush, et al · 2023
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Grounded Copilot: How Programmers Interact with Code-generating Models
Shraddha Barke, Michael B James, and Nadia Polikarpova. 2023 · 2023
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Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Fan Gao, Hang Jiang, Moritz Blum, Jinghui Lu, Yuang Jiang, and Irene Li. 2023 · 2023
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Evaluating Large Language Models in Generating Synthetic HCI Research Data: A Case Study. In ACM CHI Conference on Human Factors in Computing Systems (CHI) . 1–19
Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023 · 2023
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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
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A Qualitative Study on the Implementation Design Decisions of Developers. In IEEE/ACM International Conference on Software Engineering (ICSE) . 435–447
Jenny T Liang, Maryam Arab, Minhyuk Ko, Amy J Ko, and Thomas D LaToza. 2023 · 2023
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Large Language Models Based Automatic Synthesis of Software Specifications
Shantanu Mandal, Adhrik Chethan, Vahid Janfaza, SM Mahmud, Todd A Anderson, Javier Turek, Jesmin Jahan Tithi, and Abdullah Muzahid. 2023 · 2023
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OpenAI. 2023 · 2023
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Code LLaMA: Open Foundation Models for Code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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LLaMA 2: Open Foundation and Fine-tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
Tongshuang Wu, Haiyi Zhu, Maya Albayrak, Alexis Axon, Amanda Bertsch, Wenxing Deng, Ziqi Ding, Bill Guo, Sireesh Gururaja, Tzu-Sheng Kuo, et al · 2023
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Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding. In ACM International Conference on Intelligent User Interfaces (IUI) . 75–78
Ziang Xiao, Xingdi Yuan, Q Vera Liao, Rania Abdelghani, and Pierre-Yves Oudeyer. 2023 · 2023
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Towards an Understanding of Large Language Models in Software Engineering Tasks
Zibin Zheng, Kaiwen Ning, Jiachi Chen, Yanlin Wang, Wenqing Chen, Lianghong Guo, and Weicheng Wang. 2023 · 2023
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How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study
Ken Gu, Madeleine Grunde-McLaughlin, Andrew M McNutt, Jeffrey Heer, and Tim Althoff. 2024 · 2024
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Lost in the Middle: How Language Models Use Long Contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024 · 2024
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Reading between the Lines: Modeling User Behavior and Costs in AI-assisted Programming. In ACM CHI Conference on Human Factors in Computing Systems (CHI)
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2024 · 2024
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