The paper “An Empirical Study of API Stability and Adoption in the Android Ecosystem”, was recognized as the Most Impactful Paper from among the published papers at ICSME ’13.
Ronghui Gu is recognized for fundamental theory underlying systems verification and for synthesizing the results into realistic bug-free and hacker-resistant systems software.
Baishakhi Ray is recognized for innovative and high-quality research on software engineering and artificial intelligence, which has impacted both the academic and industrial communities.
Assistant professor Baishakhi Ray has won a VMware Early Career Faculty Award to develop machine learning tools that will improve software security. The grant program recognizes the next generation of exceptional faculty members. The gift is made in support of early-career faculty’s research and to promote excellence in teaching.
In today’s world, almost every aspect of our lives is controlled by software. Unfortunately, most software tends to be buggy, often threatening even the most safety- and security-critical software. According to a recent report, 50% of software developers’ valuable time is wasted at finding and fixing bugs costing the global economy around USD$1.1 trillion in 2016 alone.
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“The goal of my research is to address this problem and figure out how to automatically detect and fix bugs to improve software robustness, for both traditional and machine learning-based software,” said Ray, who joined the department in 2018.
In particular, her research will address two main challenges of software robustness: (i) traditional software have numerous implicit and explicit specifications; it is often difficult to know all of them in advance. (ii) With the advent of machine learning-based systems (e.g., self-driving cars), explicitly providing such specifications involving natural inputs, like image and text, is very hard.
Ray’s plan is a two-pronged approach. First, she and her team will build novel machine learning models to learn implicit specifications/rules from traditional programs and leverage these rules to detect and fix bugs automatically. However, such techniques are not easily extendable to machine learning-based systems as they follow different software paradigms (e.g., finite state machine vs. neural network). To improve the robustness of such systems, they will also devise new analysis techniques to examine the internal states of the models for potential violations.
“A successful outcome of this project will produce new techniques to detect and remediate software errors and vulnerabilities with increased accuracy to make software more secure,” said Ray.