Papers for

java developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Retrofitting code with large language models to add exception handling

Retrofitting Code Using LLMs to Support Exceptional Behavior

Abstract: Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is an essential component of software systems, allowing developers to detect and handle exceptional states that deviate from the expected program behavior. However, manually writing ERC across large codebases is tedious. We propose a novel task: retrofitting existing code with ERC. Namely, given code (without ERC) and Exceptional Behavior Tests (EBTs) (e.g., check if method throws InvalidArgumentException if null is given as the value to the argument) we aim to automatically generate missing ERC, such that the given tests pass. We design and implement Exception Coder (EXCODER) that performs context engineering to help Large Language Models (LLMs) tackle this task. EXCODER integrates static and dynamic program analysis with LLMs by providing the extracted contextual information to the LLMs. To evaluate EXCODER, we build a benchmark constructed from GitHub Java repositories, where we systematically remove ERC in 304 methods from 75 projects. Our results demonstrate that EXCODER provides an effective, though imperfect, solution to this problem in automated code generation, offering developers the first way to implement ERC following test-driven development. When combined with Qwen 2.5 Coder 32b, EXCODER achieves pass@1, 5, and 10 rates of 85.92% (12.56 percentage points over baseline), 86.18% (12.82 p.p. over baseline), and 86.51% (13.15 p.p. over baseline), respectively, on developer-written test suites. Our manual inspection of the generated code further reveals limitations of EXCODER, pointing to directions for future work.

Wed 9 SeptSoftware EngineeringComputation and Language

The gist

Software often needs special code to handle unusual or error situations, but writing this code can be boring and time-consuming. The authors created a method called EXCODER that helps large language models add this special exception-handling code automatically to existing programs when given tests that expect errors. EXCODER works by combining static analysis (looking at the code structure) and dynamic analysis (running code) with language models to better understand where and how to add these corrections. Their tests show EXCODER does a good job, though not perfect, of generating this code to pass developer-written tests.

Open → 2609.10397v1