Rust has been extensively used in software development in the past decades due to its memory safety mechanisms and gradually matured ecosystems. Enhancing the quality of Rust libraries is critical to Rust ecosystems as the libraries are often the core ...

With the tremendous advancement of deep learning techniques, object detection (OD) systems have achieved significant development. These systems, powered by deep neural networks, are now widely employed in diverse applications, including autonomous ...

Natural language inference (NLI) is a task to infer the relationship between the premise and hypothesis sentences, whose models have essential applications in the many natural language processing (NLP) fields, for example, machine reading ...

We propose a novel automatic testing method, hybrid mutation driven testing (HMT), which extends the mutation idea in natural language inference (NLI). We apply four mutation operators to achieve the hybrid mutation strategy, mutating the premise and the ...

Skeletal program enumeration (SPE) can generate a great number of test programs for validating the correctness of compilers or interpreters. The classic SPE generates programs by exhaustively enumerating all possible variable usage patterns into a given ...

Multi-sensor fusion stands as a pivotal technique in addressing numerous safety-critical tasks and applications, e.g., self-driving cars and automated robotic arms. With the continuous advancement in data-driven artificial intelligence (AI), MSF's ...

Information Retrieval Based Bug Localization (IRBL) techniques are well suited for large-scale software debugging with fewer external dependencies and lower execution costs. However, existing IRBL techniques have several challenges, including ...

• A statement-level information retrieval-based bug localization technique.
• Setting the retrieval scope can improve the effectiveness of bug localization.
• The first two-thirds of the stack trace are more advantageous for bug ...

The testing of Android applications(apps) is a challenging task due to the serious fragmentation issues and diverse usage environments. To improve the testing efficiency and collect the feedbacks from real usage scenarios, crowdsourcing has been ...

Python has been widely used to develop large-scale software systems such as distributed systems, cloud computing, artificial intelligence, and Web platforms due to its flexibility and versatility. As a kind of complex software, Python interpreter ...

The tremendous advancements in deep learning techniques have empowered question answering(QA) systems with the capability of dealing with various tasks. Many commercial QA systems, such as Siri, Google Home, and Alexa, have been deployed to assist ...

Given the prevalence of Deep Learning (DL) models in daily life, it is crucial to guarantee their reliability by DL testing. Recently, researchers have adapted mutation testing into DL testing to measure the test power of test sets. The ...

Heterogeneous defect prediction (HDP) refers to the defect prediction across projects with different metrics. Most existing HDP methods map source and target data into a common metric space where each dimension has ...

Deep neural networks (DNN) have achieved tremendous development in the past decade. While many DNN-driven software applications have been deployed to solve various tasks, they could also produce incorrect behaviors and result in massive losses. To reveal ...

Crowdsourced testing has become a popular mobile application testing method, and it is capable of simulating real usage scenarios and detecting various bugs with a large workforce. However, inspecting and classifying the overwhelming ...

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• An empirical study of crowdsourced mobile test report classification.
• ...

The dynamic typing discipline of Python allows developers to program at a high level of abstraction. However, type related bugs are commonly encountered in Python systems due to the lack of type declaration and static type checking. Especially, the ...

We aim to debug a single failing execution without the assistance from other passing/failing runs. In our context, debugging is a process with substantial uncertainty - lots of decisions have to be made such as what variables shall be inspected first. To ...

Python is a popular dynamic language that allows quick software development. However, Python program analysis engines are largely lacking. In this paper, we present a Python predictive analysis. It first collects the trace of an execution, and then ...

In many fields of software engineering, studies on human behavior have attracted a lot of attention; however, few such studies exist in automated debugging. Parnin and Orso conducted a pioneering study comparing the performance of programmers in ...

Atomicity is a fundamental property to guarantee the isolation of a work unit (<italic>i.e.</italic>, a sequence of related events in a thread) from concurrent threads. However, ensuring atomicity is often very challenging due to complex thread ...

In crowdsourced testing, users can be incentivized to perform testing tasks and report their results, and because crowdsourced workers are often paid per task, there is a financial incentive to complete tasks quickly rather than well. These reports of ...

Cross-company defect prediction (CCDP) learns a prediction model by using training data from one or multiple projects of a source company and then applies the model to the target company data. Existing CCDP methods are based on the assumption that the ...