  The growing adoption of declarative software specification languages, coupled
with their inherent difficulty in debugging, has underscored the need for
effective and automated repair techniques applicable to such languages.
Researchers have recently explored various methods to automatically repair
declarative software specifications, such as template-based repair,
feedback-driven iterative repair, and bounded exhaustive approaches. The latest
developments in large language models provide new opportunities for the
automatic repair of declarative specifications. In this study, we assess the
effectiveness of utilizing OpenAI's ChatGPT to repair software specifications
written in the Alloy declarative language. Unlike imperative languages,
specifications in Alloy are not executed but rather translated into logical
formulas and evaluated using backend constraint solvers to identify
specification instances and counterexamples to assertions. Our evaluation
focuses on ChatGPT's ability to improve the correctness and completeness of
Alloy declarative specifications through automatic repairs. We analyze the
results produced by ChatGPT and compare them with those of leading automatic
Alloy repair methods. Our study revealed that while ChatGPT falls short in
comparison to existing techniques, it was able to successfully repair bugs that
no other technique could address. Our analysis also identified errors in
ChatGPT's generated repairs, including improper operator usage, type errors,
higher-order logic misuse, and relational arity mismatches. Additionally, we
observed instances of hallucinations in ChatGPT-generated repairs and
inconsistency in its results. Our study provides valuable insights for software
practitioners, researchers, and tool builders considering ChatGPT for
declarative specification repairs.
