Projects
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UnreLyzer, A static interval analyzer for C like unreliable programs
Unrelyzer is a static analysis tool that analyzes a program defined by a subset of C (Mini-C) grammar with an addendum where each arithmetic, boolean and memory (Read/Write) operation in the program is probabilistic/unreliable in nature. This tool statically analyzes the program using Abstract Interpretation in the interval domain and determines the interval of values with a certain confidence for each program variable at each program point. This tool was built as part of my Master’s thesis.
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ProPFA, Probabilistic Path based Failure Analyzer
[code]
[paper]
This automatic tool uses a path based failure analysis approach to estimate failure probability of C programs. It is designed as an integrated framework that takes as input a C program annotated with failure assertions, the discrete ranges and probability density functions of all independent input variables. It estimates success probability of each execution path of a program separately and returns failure probability of the whole program along with a measure of confidence on the paths explored within a defined time and memory bound.
Publications
2022
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Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens,
In CHI Conference on Human Factors in Computing Systems, pp. 1-15. 2022.
Zhi Li, Maozheng Zhao, Dibyendu Das, Hang Zhao, Yan Ma, Wanyu Liu, Michel Beaudouin-Lafon, Fusheng Wang, IV Ramakrishnan, and Xiaojun Bi
2017
2016
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Failure Estimation of Behavioral Specifications,
In International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA), pp. 315-322. Springer International Publishing.
[code]
Debasmita Lohar, Anudeep Dunaboyina, Dibyendu Das, and Soumyajit Dey