Is ChatGPT the Ultimate Programming Assistant -- How far is it?

The recent progress in generative AI techniques has significantly influenced software engineering, as AI-driven methods tackle common developer challenges such as code synthesis from descriptions, program repair, and natural language summaries for existing programs. Large-scale language models (LLMs), like OpenAI's Codex, are increasingly adopted in AI-driven software engineering. ChatGPT, another LLM, has gained considerable attention for its potential as a bot for discussing source code, suggesting changes, providing descriptions, and generating code. To evaluate the practicality of LLMs as programming assistant bots, it is essential to examine their performance on unseen problems and various tasks. In our paper, we conduct an empirical analysis of ChatGPT's potential as a fully automated programming assistant, emphasizing code generation, program repair, and code summarization. Our study assesses ChatGPT's performance on common programming problems and compares it to state-of-the-art approaches using two benchmarks. Our research indicates that ChatGPT effectively handles typical programming challenges. However, we also discover the limitations in its attention span: comprehensive descriptions can restrict ChatGPT's focus and impede its ability to utilize its extensive knowledge for problem-solving. Surprisingly, we find that ChatGPT's summary explanations of incorrect code provide valuable insights into the developer's original intentions. This insight can be served as a foundation for future work addressing the oracle problem. Our study offers valuable perspectives on the development of LLMs for programming assistance, specifically by highlighting the significance of prompt engineering and enhancing our comprehension of ChatGPT's practical applications in software engineering.

MEMO: Coverage-guided Model Generation For Deep Learning Library Testing

Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality assurance of these libraries is critical to the dependable deployment of DL applications. A few techniques have thereby been proposed to test DL libraries by generating DL models as test inputs. Then these techniques feed those DL models to DL libraries for making inferences, in order to exercise DL libraries modules related to a DL model's execution. However, the test effectiveness of these techniques is constrained by the diversity of generated DL models. Our investigation finds that these techniques can cover at most 11.7% of layer pairs (i.e., call sequence between two layer APIs) and 55.8% of layer parameters (e.g., "padding" in Conv2D). As a result, we find that many bugs arising from specific layer pairs and parameters can be missed by existing techniques. In view of the limitations of existing DL library testing techniques, we propose MEMO to efficiently generate diverse DL models by exploring layer types, layer pairs, and layer parameters. MEMO: (1) designs an initial model reduction technique to boost test efficiency without compromising model diversity; and (2) designs a set of mutation operators for a customized Markov Chain Monte Carlo (MCMC) algorithm to explore new layer types, layer pairs, and layer parameters. We evaluate MEMO on seven popular DL libraries, including four for model execution (TensorFlow, PyTorch and MXNet, and ONNX) and three for model conversions (Keras-MXNet, TF2ONNX, ONNX2PyTorch). The evaluation result shows that MEMO outperforms recent works by covering 10.3% more layer pairs, 15.3% more layer parameters, and 2.3% library branches. Moreover, MEMO detects 29 new bugs in the latest version of DL libraries, with 17 of them confirmed by DL library developers, and 5 of those confirmed bugs have been fixed.