Enormous risks and hidden dangers of information security exist in the applications of Internet of Things (IoT) technologies. To secure IoT software systems, software engineers have to deploy advanced security software such as Intrusion Detection Systems (...

There has been a recent explosion of research on Large Language Models (LLMs) for software engineering tasks, in particular code generation. However, results from LLMs can be highly unstable; non-deterministically returning very different code for the ...

Large Language Models (LLMs) have shown impressive abilities in code generation. Chain-of-Thought (CoT) prompting is the state-of-the-art approach to utilizing LLMs. CoT prompting asks LLMs first to generate CoTs (i.e., intermediate natural language ...

Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Github Copilot are not accepted ...

In the rapidly evolving landscape of software engineering, the integration of Artificial Intelligence (AI) into the Software Development Life-Cycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift towards ...

What if end users could own the software development life cycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that Generative Artificial Intelligence ...

Large language models (LLMs) have shown great success in code generation. LLMs take as the input a prompt and output the code. How to make prompts (i.e., Prompting Techniques) is a key question. Existing prompting techniques are designed for natural ...

Genetic Improvement (GI) uses search-based optimisation algorithms to automatically improve software with respect to both functional and non-functional properties. Our previous work showed that Regression Test Selection (RTS) can help speed up the use of ...

Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire a broad spectrum of open-domain knowledge. Besides, their evaluation ...

Despite its effectiveness in ensuring software quality, code review remains a labor-intensive and time-consuming task. In order to alleviate this burden on developers, researchers have proposed the automation of code review activities, particularly ...

Large Language Models (LLMs) have received much recent attention due to their human-level accuracy. While existing works mostly focus on either improving accuracy or testing accuracy robustness, the computation efficiency of LLMs, which is of paramount ...

Code models have made significant advancements in code intelligence by encoding knowledge about programming languages. While previous studies have explored the capabilities of these models in learning code syntax, there has been limited investigation on ...

Smart contracts are becoming appealing targets for hackers because of the vast amount of cryptocurrencies under their control. Asset loss due to the exploitation of smart contract codes has increased significantly in recent years. To guarantee that smart ...

In recent years, dynamic languages such as Python have become popular due to their flexibility and productivity. The lack of static typing makes programs face the challenges of fixing type errors, early bug detection, and code understanding. To alleviate ...

A smart contract is a kind of code deployed on the blockchain that executes automatically once an event triggers a clause in the contract. Since smart contracts involve businesses such as asset transfer, they are more vulnerable to attacks, so it is ...

In the software engineering (SE) community, deep learning (DL) has recently been applied to many source code processing tasks, achieving state-of-the-art results. Due to the poor interpretability of DL models, their security vulnerabilities require ...

To date, the users of test-driven program repair tools suffer from the overfitting problem; a generated patch may pass all available tests without being correct. In the existing work, users are treated as merely passive consumers of the tests. However, ...

Mocking is an essential unit testing technique for isolating the class under test from its dependencies. Developers often leverage mocking frameworks to develop stub code that specifies the behaviors of mock objects. However, developing and maintaining ...

Developers often perform repetitive code editing activities (up to 70%) for various reasons (e.g., code refactoring) during software development. Many deep learning (DL) models have been proposed to automate code editing by learning from the code editing ...

Evaluation is the foundation of automated program repair (APR), as it provides empirical evidence on strengths and weaknesses of APR techniques. However, the reliability of such evaluation is often threatened by various introduced biases. Consequently, ...