DevBench: A Comprehensive Benchmark for Software Development

• URL: http://arxiv.org/abs/2403.08604v2
• Date: Fri, 15 Mar 2024 13:23:34 GMT
• Title: DevBench: A Comprehensive Benchmark for Software Development
• Authors: Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen,
• Abstract summary: DevBench is a benchmark that evaluates large language models (LLMs) across various stages of the software development lifecycle. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.
• Score: 72.24266814625685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of programming, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programming activities. To this end, we propose DevBench, a comprehensive benchmark that evaluates LLMs across various stages of the software development lifecycle, including software design, environment setup, implementation, acceptance testing, and unit testing. DevBench features a wide range of programming languages and domains, high-quality data collection, and carefully designed and verified metrics for each task. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Analyses reveal that models struggle with understanding the complex structures in the repository, managing the compilation process, and grasping advanced programming concepts. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications. Our benchmark is available at https://github.com/open-compass/DevBench

Related papers

• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code [29.178248778212588]
  ComplexCodeEval is a benchmark designed to assess large language models (LLMs) in various development tasks. It includes 3,897 Java samples and 7,184 Python samples from high-star GitHub repositories.
  arXiv  Detail & Related papers  (2024-09-16T13:43:04Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• A Survey on Large Language Models for Code Generation [9.555952109820392]
  Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks. This survey aims to bridge the gap between academia and practical development by providing a comprehensive and up-to-date literature review.
  arXiv  Detail & Related papers  (2024-06-01T17:48:15Z)
• CodeEditorBench: Evaluating Code Editing Capability of Large Language Models [49.387195629660994]
  Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability. We introduce CodeEditorBench, an evaluation framework designed to rigorously assess the performance of LLMs in code editing tasks. We curate diverse coding challenges and scenarios from five sources, covering various programming languages, complexity levels, and editing tasks.
  arXiv  Detail & Related papers  (2024-04-04T15:49:49Z)
• CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios [25.085449990951034]
  We introduce CoderUJB, a new benchmark designed to evaluate large language models (LLMs) across diverse Java programming tasks. Our empirical study on this benchmark investigates the coding abilities of various open-source and closed-source LLMs. The findings indicate that while LLMs exhibit strong potential, challenges remain, particularly in non-functional code generation.
  arXiv  Detail & Related papers  (2024-03-28T10:19:18Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming [12.355284125578342]
  Large Language Models (LLMs) have become a focal point in modern software development. LLMs offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, each system requires the LLM to be honed to its set of workspaces to ensure the best performance.
  arXiv  Detail & Related papers  (2024-02-22T03:51:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.