Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, ...

Code summarization aims to automatically generate natural language descriptions for code, and has become a rapidly expanding research area in the past decades. Unfortunately, existing approaches mainly focus on the “one-to-one” mapping from ...

Large language models like BERT and GPT possess significant capabilities and potential impacts across various applications. Software engineers often use these models for code-related tasks, including generating, debugging, and summarizing code. ...

Binary Code Similarity Detection (BCSD) is a fundamental binary analysis technique in the area of software security. Recently, advanced deep learning algorithms are integrated into BCSD platforms to achieve superior performance on well-known benchmarks. ...

(Source) code summarization aims to automatically generate succinct natural language summaries for given code snippets. Such summaries play a significant role in promoting developers to understand and maintain code. Inspired by neural machine translation, ...

Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS ...

Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from ...

Application review event inference aims to assess the effectiveness of application problems in response to user actions, which enables application developers to promptly discover and address potential issues in various applications, thereby improving ...

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the <italic>representation</italic> of ...

Deep learning-based vulnerability prediction approaches are proposed to help under-resourced security practitioners to detect vulnerable functions. However, security practitioners still do not know what type of vulnerabilities correspond to a given ...

While massive efforts have been investigated in adversarial testing of convolutional neural networks (CNN), testing for recurrent neural networks (RNN) is still limited and leaves threats for vast sequential application domains. In this paper, we propose ...

One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature engineering prior to running DL. For the specific case of deep learning for ...

<italic>Context:</italic> In empirical software engineering, crossover designs are popular for experiments comparing software engineering techniques that must be undertaken by human participants. However, their value depends on the correlation (<inline-...

Deep learning has gained tremendous success and great popularity in the past few years. However, deep learning systems are suffering several inherent weaknesses, which can threaten the security of learning models. Deep learning&#x2019;s wide use further ...

Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, hacking, information loss and system failure. A variety of approaches have been developed to try and detect the most likely ...

Bug localization is the task of determining which source code entities are relevant to a bug report. Manual bug localization is labor intensive since developers must consider thousands of source code entities. Current research builds bug localization ...

Machine learning classifiers have recently emerged as a way to predict the introduction of bugs in changes made to source code files. The classifier is first trained on software history, and then used to predict if an impending change causes a bug. ...

Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially ...

Socio-technical congruence is an approach that measures coordination by examining the alignment between the technical dependencies and the social coordination in the project. We conduct a case study of coordination in the IBM Rational Team Concert ...

Bayesian networks, which can combine sparse data, prior assumptions and expert judgment into a single causal model, have already been used to build software effort prediction models. We present such a model of an Extreme Programming environment and show ...