相关论文: Learning to Recognize Actionable Static Code Warni…
Knowledge-based systems reason over some knowledge base. Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses active learning methods for acquiring knowledge for "static…
Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should not be ignored, we suggest…
The use of static analysis tools has gained increasing popularity among developers in the last few years. However, the widespread adoption of static analysis tools is hindered by their high false alarm rates. Previous studies have…
Static analysis tools have gained popularity among developers for finding potential bugs, but their widespread adoption is hindered by the accomnpanying high false alarm rates (up to 90%). To address this challenge, previous studies…
Static Code Analysis (SCA) tools, while invaluable for identifying potential coding problems, functional bugs, or vulnerabilities, often generate an overwhelming number of warnings, many of which are non-actionable. This overload of alerts…
Static Analysis (SA) tools are used to identify potential weaknesses in code and fix them in advance, while the code is being developed. In legacy codebases with high complexity, these rules-based static analysis tools generally report a…
Despite their ability to aid developers in detecting potential defects early in the software development life cycle, static analysis tools often suffer from precision issues (i.e., high false positive rates of reported alarms). To improve…
Static bug detection tools help developers detect problems in the code, including bad programming practices and potential defects. Recent efforts to integrate static bug detectors in modern software development workflows, such as in code…
Static analysis tools have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers' productivity and confidence in the tools. Previous research efforts have explored learning-based…
Static analysis tools are frequently used to detect potential vulnerabilities in software systems. However, an inevitable problem of these tools is their large number of warnings with a high false positive rate, which consumes time and…
Static code analysis (SCA) tools are widely used as effective ways to detect bugs and vulnerabilities in software systems. However, the reports generated by these tools often contain a large number of non-actionable findings, which can…
Automatic static analysis tools (ASATs), such as Findbugs, have a high false alarm rate. The large number of false alarms produced poses a barrier to adoption. Researchers have proposed the use of machine learning to prune false alarms and…
Programmers have long ignored warnings, especially those generated by static analysis tools, due to the potential for false-positives. In some cases, warnings may be indicative of larger issues, but programmers may not understand how a…
Static analysis tools are widely used to detect bugs, vulnerabilities, and code smells. Traditionally, developers must resolve these warnings manually. Because this process is tedious, developers sometimes ignore warnings, leading to an…
Static bug detection tools help developers detect code problems. However, it is known that they remain underutilized due to various reasons. Recent advances to incorporate static bug detectors in modern software development workflows can…
Web applications continue to be a favorite target for hackers due to a combination of wide adoption and rapid deployment cycles, which often lead to the introduction of high impact vulnerabilities. Static analysis tools are important to…
Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analyses is difficult and requires high expertise, which results…
Static analysis tools are commonly used to detect defects before the code is released. Previous research has focused on their overall effectiveness and their ability to detect defects. However, little is known about the usage patterns of…
In order to ensure the quality of software and prevent attacks from hackers on critical systems, static analysis tools are frequently utilized to detect vulnerabilities in the early development phase. However, these tools often report a…
Neural networks are vulnerable to a wide range of erroneous inputs such as adversarial, corrupted, out-of-distribution, and misclassified examples. In this work, we train a linear SVM classifier to detect these four types of erroneous data…