Maksim Gomov, Sai Kopparthi.A Survey on Fuzzing Techniques with Professor Hao Chen for Computer and Information security (ECS235A)
In the evaluation of software for exploitable vulner-abilities and other faults, automated testing is heavily utilized. One automated testing technique that has been proven effective is fuzz testing, or fuzzing, a technique which provides inputs to a program under test in order to exercise and test different parts of the program and attempt to induce undefined or undesired behavior. In this survey, we separate fuzzing techniques into three approaches: black box, white box, and grey box. For each of these approaches, we describe the motivation for utilizing the particular approach, the applicability of the approach in the context of fuzzable programs, and describe up-to-date techniques which fall under each approach. Additionally, at least one technique surveyed in each approach incorporates machine learning, so these use cases are described as well.
Shaikh Ismail, Sai Kopparthi. Automatically restructuring Java comments with Assis.Professor Aditya Thakur for Automated Reasoning and Program Analysis (ECS289C)
Given that there are a lot of real-world software projectswhere the comments could be structured, our intention isto transform code comments for methods and classes to amore structured form. Programmers often refer to identifiers(variables, methods, classes) when they are adding com- ments(including inline), which means the comments are a mix of English and source code tokens. More often than not,the programmers don’t follow comment guidelines and their comments do not follow any specific pattern because of individual comment style. This non-conformity in commenting style makes code reviews difficult. So, we are motivated to transform a non-structured comment block to a structured comments.
Sai Kopparthi, Suhas Krishna , Shivang Soni.Human Activity Recognition with Professor Cho-Jui Hsieh for STA 141
The dataset is taken from UCI Repository named as Heterogeneity Activity Recognition DataSet. The data is characterized as Multivariate and is used for an associated task like Clustering and classification. The dataset contains the readings of two motion sensors commonly found in smart-phones. Reading were recorded while users executed activities scripted in no specific order carrying smartwatches and smartphones. We need to recognize the human activities of the different user by using Classification and clustering techniques.