Related papers: A Comparative Study of Vulnerability Reporting by …
Software Vulnerability Prediction (SVP) is a data-driven technique for software quality assurance that has recently gained considerable attention in the Software Engineering research community. However, the difficulties of preparing…
In current research, there are contrasting results about the applicability of software source code metrics as features for defect prediction models. The goal of the paper is to evaluate the adoption of software metrics in models for…
Open source software ecosystems consist of thousands of interdependent libraries, which users can combine to great effect. Recent work has pointed out two kinds of risks in these systems: that technical problems like bugs and…
While reusing third-party libraries (TPL) facilitates software development, its chaotic management has brought great threats to software maintenance and the unauthorized use of source code also raises ethical problems such as misconduct on…
Large organizations have diverse product offerings to meet various business needs. To increase revenue, its common these days to offer software products as integrated product suite(s) rather than individual products. Creating and…
Increasing interest in securing the Android ecosystem has spawned numerous efforts to assist app developers in building secure apps. These efforts have resulted in tools and techniques capable of detecting vulnerabilities (and malicious…
Developers usually use TPLs to facilitate the development of the projects to avoid reinventing the wheels, however, the vulnerable TPLs indeed cause severe security threats. The majority of existing research only considered whether projects…
Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false…
The present business network infrastructure is quickly varying with latest servers, services, connections, and ports added often, at times day by day, and with a uncontrollably inflow of laptops, storage media and wireless networks. With…
The popularity of JavaScript has lead to a large ecosystem of third-party packages available via the npm software package registry. The open nature of npm has boosted its growth, providing over 800,000 free and reusable software packages.…
The open-source software (OSS) ecosystem suffers from security threats caused by malware.However, OSS malware research has three limitations: a lack of high-quality datasets, a lack of malware diversity, and a lack of attack campaign…
Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for…
Security vulnerabilities introduced by applications using third-party dependencies are on the increase, caused by the emergence of large ecosystems of libraries such as the NPM packages for JavaScript. Nowadays, libraries depend on each…
Dependency solving is a hard (NP-complete) problem in all non-trivial component models due to either mutually incompatible versions of the same packages or explicitly declared package conflicts. As such, software upgrade planning needs to…
Python applications depend on third-party native libraries that may be vendored within package distributions or installed on the host system. When vulnerabilities are discovered in these native libraries, determining which Python packages…
Software reuse may result in software bloat when significant portions of application dependencies are effectively unused. Several tools exist to remove unused (byte)code from an application or its dependencies, thus producing smaller…
A system vulnerability analysis technique (SVAT) for complex mission critical systems (CMCS) was developed in response to the need to be able to conduct penetration testing on large industrial systems which cannot be taken offline or risk…
Existing innovation metrics inadequately capture software innovation, creating blind spots for researchers and policymakers seeking to understand and foster technological innovation in an increasingly software-defined economy. This paper…
The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number…
Much of the success of modern software development can be attributed to code reuse. The ability to reuse existing functionality via third-party dependencies has enabled massive gains in productivity, but for a long time the dominant…