8 citations · 13 across the 11 of their papers we have counts for
11 papers
From Failing to Passing: Evolving Natural Language Prompt Optimization Rules for LLM Code Generation
Amal Akli, Melissa Akli, Cedric Richter +2
Large language models are known to be sensitive to prompt formulation. Even minor variations in wording can substantially degrade performance. This sensitivity reveals an opportuni…
Round-Trip Mutation Testing: Translating Code to Natural Language Intent and back
Asma Hamidi, Cedric Richter, Ahmed Khanfir +1
This paper presents Round-Trip Mutation Testing (RTM), a novel approach that generates mutants from LLM mistranslations between a program code and its intent. Leveraging the genera…
Prompt Coverage Adequacy
Florian Tambon, Michael Konstantinou, Cedric Richter +3
In recent years, it has become increasingly evident that large language models (LLMs) and autonomous agents raise the level of abstraction in software development by shifting the f…
Underspecification does not imply Incoherence: The Risks of Semantic Collapse in Coding Models
Cedric Richter, Mike Papadakis
Large Language Models (LLMs) have become increasingly effective at generating code when task descriptions are clear and precise. Yet, in practice, user-provided task descriptions a…
Automatically Evolving Prompt Guidelines for Task-Specific Optimization
Cedric Richter, Salah Ghamizi, Mike Papadakis
For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints. In practice, however, user queries are often underspec…
Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?
Cedric Richter, Heike Wehrheim
Automatic software verifiers have become increasingly effective at the task of checking software against (formal) specifications. Yet, their adoption in practice has been hampered…
Parallel Program Analysis on Path Ranges
Jan Haltermanna, Marie-Christine Jakobs, Cedric Richter +1
Symbolic execution is a software verification technique symbolically running programs and thereby checking for bugs. Ranged symbolic execution performs symbolic execution on progra…
Can ChatGPT support software verification?
Christian Janßen, Cedric Richter, Heike Wehrheim
Large language models have become increasingly effective in software engineering tasks such as code generation, debugging and repair. Language models like ChatGPT can not only gene…
Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes
Cedric Richter, Heike Wehrheim
Real bug fixes found in open source repositories seem to be the perfect source for learning to localize and repair real bugs. However, the absence of large scale bug fix collection…
TSSB-3M: Mining single statement bugs at massive scale
Cedric Richter, Heike Wehrheim
Single statement bugs are one of the most important ingredients in the evaluation of modern bug detection and automatic program repair methods. By affecting only a single statement…
DeepMutants: Training neural bug detectors with contextual mutations
Cedric Richter, Heike Wehrheim
Learning-based bug detectors promise to find bugs in large code bases by exploiting natural hints such as names of variables and functions or comments. Still, existing techniques t…