Code quality assessment using transformers

• URL: http://arxiv.org/abs/2309.09264v1
• Date: Sun, 17 Sep 2023 12:59:59 GMT
• Title: Code quality assessment using transformers
• Authors: Mosleh Mahamud and Isak Samsten
• Abstract summary: In this work we investigate the use of CodeBERT to automatically assign quality score to Java code. We explore the accuracy of the models on a novel dataset for code quality assessment. We find that code quality to some extent is predictable and that transformer based models using task adapted pre-training can solve the task more efficiently than other techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatically evaluate the correctness of programming assignments is rather straightforward using unit and integration tests. However, programming tasks can be solved in multiple ways, many of which, although correct, are inelegant. For instance, excessive branching, poor naming or repetitiveness make the code hard to understand and maintain. These subjective qualities of code are hard to automatically assess using current techniques. In this work we investigate the use of CodeBERT to automatically assign quality score to Java code. We experiment with different models and training paradigms. We explore the accuracy of the models on a novel dataset for code quality assessment. Finally, we assess the quality of the predictions using saliency maps. We find that code quality to some extent is predictable and that transformer based models using task adapted pre-training can solve the task more efficiently than other techniques.

Related papers

• Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning [50.26965628047682]
  Adapting pre-trained models to open classes is a challenging problem in machine learning. In this paper, we consider combining the advantages of both and come up with a test-time prompt tuning approach. Our proposed method outperforms all comparison methods on average considering both base and new classes.
  arXiv  Detail & Related papers  (2024-08-29T12:34:01Z)
• Towards Adapting Reinforcement Learning Agents to New Tasks: Insights from Q-Values [8.694989771294013]
  Policy gradient methods can still be useful in many domains as long as we can wrangle with how to exploit them in a sample efficient way. We explore the chaotic nature of DQNs in reinforcement learning, while understanding how the information that they retain when trained can be repurposed for adapting a model to different tasks.
  arXiv  Detail & Related papers  (2024-07-14T21:28:27Z)
• Improving Automated Code Reviews: Learning from Experience [12.573740138977065]
  This study investigates whether higher-quality reviews can be generated from automated code review models. We find that experience-aware oversampling can increase the correctness, level of information, and meaningfulness of reviews.
  arXiv  Detail & Related papers  (2024-02-06T07:48:22Z)
• Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation [1.0878040851638]
  We introduce a novel deep-learning architecture, named DISCOREV, which employs cross-task knowledge distillation to address these tasks simultaneously. We show that our approach generates better review comments, as measured by the BLEU score, as well as more accurate code refinement according to the CodeBLEU score.
  arXiv  Detail & Related papers  (2024-02-03T07:02:22Z)
• A Static Evaluation of Code Completion by Large Language Models [65.18008807383816]
  Execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple programming problems. static analysis tools such as linters, which can detect errors without running the program, haven't been well explored for evaluating code generation models. We propose a static evaluation framework to quantify static errors in Python code completions, by leveraging Abstract Syntax Trees.
  arXiv  Detail & Related papers  (2023-06-05T19:23:34Z)
• Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study [4.438873396405334]
  We aim to answer whether making code easier to understand through using contextual data improves the performance of pre-trained code language models for the task of code completion. For comments, we find that the models perform better in the presence of multi-line comments.
  arXiv  Detail & Related papers  (2023-04-24T17:09:14Z)
• CodeExp: Explanatory Code Document Generation [94.43677536210465]
  Existing code-to-text generation models produce only high-level summaries of code. We conduct a human study to identify the criteria for high-quality explanatory docstring for code. We present a multi-stage fine-tuning strategy and baseline models for the task.
  arXiv  Detail & Related papers  (2022-11-25T18:05:44Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)
• Generating Bug-Fixes Using Pretrained Transformers [11.012132897417592]
  We introduce a data-driven program repair approach which learns to detect and fix bugs in Java methods mined from real-world GitHub. We show that pretraining on source code programs improves the number of patches found by 33% as compared to supervised training from scratch. We refine the standard accuracy evaluation metric into non-deletion and deletion-only fixes, and show that our best model generates 75% more non-deletion fixes than the previous state of the art.
  arXiv  Detail & Related papers  (2021-04-16T05:27:04Z)
• Adaptive Task Sampling for Meta-Learning [79.61146834134459]
  Key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time. We propose an adaptive task sampling method to improve the generalization performance.
  arXiv  Detail & Related papers  (2020-07-17T03:15:53Z)
• Frustratingly Simple Few-Shot Object Detection [98.42824677627581]
  We find that fine-tuning only the last layer of existing detectors on rare classes is crucial to the few-shot object detection task. Such a simple approach outperforms the meta-learning methods by roughly 220 points on current benchmarks.
  arXiv  Detail & Related papers  (2020-03-16T00:29:14Z)

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.