Context: This paper provides an in-depth examination of the generation and evaluation of Metamorphic Relations (MRs) using GPT models developed by OpenAI, with a particular focus on the capabilities of GPT-4 in software testing environments. Objective: The aim is to examine the quality of MRs produced by GPT-3.5 and GPT-4 for a specific System Under Test (SUT) adopted from an earlier study, and to introduce and apply an improved set of evaluation criteria for a diverse range of SUTs. Method: The initial phase evaluates MRs generated by GPT-3.5 and GPT-4 using criteria from a prior study, followed by an application of an enhanced evaluation framework on MRs created by GPT-4 for a diverse range of nine SUTs, varying from simple programs to complex systems incorporating AI/ML components. A custom-built GPT evaluator, alongside human evaluators, assessed the MRs, enabling a direct comparison between automated and human evaluation methods. Results: The study finds that GPT-4 outperforms GPT-3.5 in generating accurate and useful MRs. With the advanced evaluation criteria, GPT-4 demonstrates a significant ability to produce high-quality MRs across a wide range of SUTs, including complex systems incorporating AI/ML components. Conclusions: GPT-4 exhibits advanced capabilities in generating MRs suitable for various applications. The research underscores the growing potential of AI in software testing, particularly in the generation and evaluation of MRs, and points towards the complementarity of human and AI skills in this domain.

Artificial intelligence (AI) has revolutionized software engineering (SE) by enhancing software development efficiency. The advent of pre-trained models (PTMs) leveraging transfer learning has significantly advanced AI for SE. However, existing PTMs that operate on individual code tokens suffer from several limitations: They are costly to train and fine-tune; and they rely heavily on labeled data for fine-tuning on task-specific datasets. In this paper, we present TransformCode, a novel framework that learns code embeddings in a contrastive learning manner. Our framework is encoder-agnostic and language-agnostic, which means that it can leverage any encoder model and handle any programming language. We also propose a novel data-augmentation technique called abstract syntax tree (AST) transformation, which applies syntactic and semantic transformations to the original code snippets, to generate more diverse and robust samples for contrastive learning. Our framework has several advantages over existing methods: (1) It is flexible and adaptable, because it can easily be extended to other downstream tasks that require code representation (such as code-clone detection and classification); (2) it is efficient and scalable, because it does not require a large model or a large amount of training data, and it can support any programming language; (3) it is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives; and (4) it can also adjust the number of encoder parameters based on computing resources. We evaluate our framework on several code-related tasks, and demonstrate its effectiveness and superiority over the state-of-the-art methods such as SourcererCC, Code2vec, and InferCode.