26,098 papers · page 176 of 1,305
Code fragments from developer forums often migrate to applications due to the code reuse practice. Owing to the incomplete nature of such programs, analyzing them to early determine the presence of potential vulnerabilities is challenging. In this work, we introduce NeuralPDA, a …
Python programming language has gained enor-mous popularity in the past decades. While its flexibility signifi-cantly improves software development productivity, the dynamic typing feature challenges software maintenance and quality assurance. To facilitate programming and type e…
Database-backed applications are widely used. To effectively test these applications, one needs to design not only user inputs but also database states, which imposes unique challenges. First, valid database states have to satisfy complicated constraints determined by application…
Nowadays, many applications do not exist independently but rely on various frameworks or libraries. The frequent evolution and the complex implementation of framework APIs induce lots of unexpected post-release crashes. Starting from the crash stack traces, existing approaches ei…
Today, more than 3 million websites rely on online advertising revenue. Despite the monetary incentives, ads often frustrate users by disrupting their experience, interrupting content, and slowing browsing. To improve ad experiences, leading media associations define Better Ads S…
Modern web applications make extensive use of API calls to update the UI state in response to user events or server-side changes. For such applications, API-level testing can play an important role, in-between unit-level testing and UI-level (or end-to-end) testing. Existing API …
Android API-related compatibility issues have be-come a severe problem and significant challenge for app devel-opers due to the well-known Android fragmentation issues. To address this problem, many effective approaches such as app-based and API lifetime-based methods have been p…
Deep learning (DL) has attracted wide attention and has been widely deployed in recent years. As a result, more and more research efforts have been dedicated to testing DL libraries and frameworks. However, existing work largely overlooked one crucial component of any DL system, …
Developing a software project using multiple languages together has been a dominant practice for years. Yet it remains unclear what issues developers encounter during the development, which challenges cause the issues, and what solutions developers receive. In this paper, we aim …
Recent progress in Deep Learning (DL) has sparked interest in using DL to detect software vulnerabilities automatically and it has been demonstrated promising results at detecting vulnerabilities. However, one prominent and practical issue for vulnerability detection is data imba…
Sustainable Open Source Software (OSS) projects are characterized by the ability to attract new project members and maintain an energetic project community. Building sustainable OSS projects from a nascent state requires effective project governance and socio-technical structure …
Deep learning (DL) Systems have been widely used in various domains. Similar to traditional software, DL system evolution may also incur regression faults. To find the regression faults between versions of a DL system, we propose a novel regression fuzzing technique called DRFuzz…
Modern systems generate a massive amount of logs to detect and diagnose system faults, which incurs expensive storage costs and runtime overhead. After investigating real-world production logs, we observe that most of the logging overhead is due to a small number of log templates…
Automatic static analysis tools (ASATs) detect source code violations to static analysis rules and are usually used as a guard for source code quality. The adoption of ASATs, however, is often challenged because of several problems such as a large number of false alarms, invalid …
Continuous game quality assurance is of great importance to satisfy the increasing demands of users. To respond to game issues reported by users timely, game com-panies often create and maintain a large number of releases, updates, and tweaks in a short time. Regression testing i…
Software vulnerabilities have posed huge threats to the cyberspace security, and there is an increasing demand for automated vulnerability detection (VD). In recent years, deep learning-based (DL-based) vulnerability detection systems have been proposed for the purpose of automat…
Various deep neural network (DNN) coverage criteria have been proposed to assess DNN test inputs and steer input mutations. The coverage is characterized via neurons having certain outputs, or the discrepancy between neuron outputs. Nevertheless, recent research indicates that ne…
Exception handling is a mechanism in modern programming languages. Studies have shown that the exception handling code is error-prone. However, there is still limited research on detecting exception handling bugs, especially for C++ programs. To tackle the issue, we try to precis…
Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the privacy of CFL can be compromised by many attacks, such as free-rider attacks, adversarial attacks, gradient l…
With the increasing disclosure of vulnerabilities in open-source software, software composition analysis (SCA) has been widely applied to reveal third-party libraries and the associated vulnerabilities in software projects. Beyond the revelation, SCA tools adopt various remediati…