When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
Open Access
- 12 April 2026
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 297-309
Abstract
No abstract availableAll Related Versions
This publication has 23 references indexed in Scilit:
- Enhancing software development effort estimation with a cloud-based data framework using use case points, fuzzy logic, and machine learningDiscover Computing, 2025
- Enhancing software effort estimation through reinforcement learning-based project management-oriented feature selectionInternational Journal of Managing Projects in Business, 2025
- Towards an understanding of large language models in software engineering tasksEmpirical Software Engineering, 2024
- GitHub Copilot AI pair programmer: Asset or Liability?Journal of Systems and Software, 2023
- Empirical research on requirements quality: a systematic mapping studyRequirements Engineering, 2022
- Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluationSoftware: Practice and Experience, 2021
- A systematic review of machine learning techniques for software fault predictionApplied Soft Computing, 2015
- Reasoning with contextual requirements: Detecting inconsistency and conflictsInformation and Software Technology, 2013
- IEEE Recommended Practice for Software Requirements Specifications2008
- Making inconsistency respectable in software developmentJournal of Systems and Software, 2001