Arghavan Moradi Dakhel

Michel C. Desmarais

Giuliano Antoniol

2024-03-13

ArXiv (preprint)

Assessing the Security of GitHub Copilot Generated Code - A Targeted Replication Study

Vahid Majdinasab

Michael Joshua Bishop

Shawn Rasheed

Arghavan Moradi Dakhel

Amjed Tahir

2023-11-18

ArXiv (preprint)

Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

Arghavan Moradi Dakhel

Amin Nikanjam

Vahid Majdinasab

Michel C. Desmarais

One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenan… (see more)ce costs. The goal of automated test generation tools is to ease the development of tests by suggesting efficient bug-revealing tests. Recently, researchers have leveraged Large Language Models (LLMs) of code to generate unit tests. While the code coverage of generated tests was usually assessed, the literature has acknowledged that the coverage is weakly correlated with the efficiency of tests in bug detection. To improve over this limitation, in this paper, we introduce MuTAP for improving the effectiveness of test cases generated by LLMs in terms of revealing bugs by leveraging mutation testing. Our goal is achieved by augmenting prompts with surviving mutants, as those mutants highlight the limitations of test cases in detecting bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate their performance on different benchmarks. Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets. Among these, 17% remained undetected by both the current state-of-the-art fully automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% on synthetic buggy code, outperforming all other approaches in our evaluation. Our findings suggest that although LLMs can serve as a useful tool to generate test cases, they require specific post-processing steps to enhance the effectiveness of the generated test cases which may suffer from syntactic or functional errors and may be ineffective in detecting certain types of bugs and testing corner cases PUTs.

2023-08-31

ArXiv (preprint)

Dev2vec: Representing Domain Expertise of Developers in an Embedding Space

Arghavan Moradi Dakhel

Michel C. Desmarais

2023-07-01

Information and Software Technology (published)

GitHub Copilot AI pair programmer: Asset or Liability?

Arghavan Moradi Dakhel

Vahid Majdinasab

Amin Nikanjam

Michel C. Desmarais

Z. Jiang

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called… (see more) Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.

2023-01-01

J. Syst. Softw. (published)