Advances in AI-Driven Software Development

The field of software development is witnessing significant advancements with the integration of large language models (LLMs) and artificial intelligence (AI). Researchers are exploring new methods to evaluate and improve the performance of AI-powered coding assistants, with a focus on accuracy, reliability, and usability. A key challenge in this area is the development of robust benchmarks and evaluation metrics to assess the capabilities of these models. Recent studies have also highlighted the importance of semantic understanding and code comprehension in LLMs, with implications for applications such as reverse engineering and code generation. Furthermore, researchers are investigating AI-driven modernization of legacy code, with promising results in improving code quality and reducing complexity. Noteworthy papers in this area include:

  • SWE-PolyBench, which introduces a novel benchmark for evaluating coding agents across multiple programming languages, and
  • Code Reborn, which presents an AI-driven approach to modernizing legacy COBOL code into Java with impressive accuracy and complexity reduction.

Sources

Quality evaluation of Tabby coding assistant using real source code snippets

SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents

Automated Testing of COBOL to Java Transformation

The Code Barrier: What LLMs Actually Understand?

Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java

Themisto: Jupyter-Based Runtime Benchmark

Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation

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