Computer Science Department Thesis Defense - Subrata Das

Event Date: 
Friday, May 10, 2024 - 10:00am to 11:30am EDT
Event Location: 
Virtual
Event Contact Name: 
Rachael Wang
Event Contact E-mail: 

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Subrata Das

Thesis title: Exploring Name-based Bug Detection in Python

Abstract: Names of source code elements provide useful contextual information about the code and development tasks. Prior studies leverage the similarity between the names of arguments and method parameters to detect bugs that are caused by accidentally swapping arguments while calling methods. This requires establishing the mapping between method calls and their definitions. However, it is a challenging task to establish the mapping because of the complexity involved with the process (e.g., missing external libraries). This thesis aims to understand the performance of name-based argument-related bug detection techniques in Python, a popular general-purpose, statically typed programming language.

Towards this direction, this thesis conducts a study that first investigates the similarity between arguments and their method parameters in Python code. This thesis establishes the mapping between method calls to their definitions and evaluates the performance of existing name-based techniques to detect swapping argument-related bugs in Python. Finally, a technique has been developed that uses argument usage patterns and their expression types with name-based similarity matching to improve the performance of detecting argument-related bugs. Evaluation of the proposed technique with a large collection of open-source Python projects showed that the technique can detect argument-related bugs with high accuracy even when the function definitions are missing. One potential solution to prevent argument-related bugs from occurring is to recommend arguments as a developer types the code. Thus, the second part of the thesis focuses on argument recommendation. In particular, this thesis investigates the efficacy of large language models in recommending arguments of API method calls.

Committee Members:
Dr. Muhammad Asaduzzaman (supervisor, committee chair), Dr. Salimur Choudhury (co-supervisor, Queen’s University), Dr. Garima Bajwa, Dr. Zubair Fadlullah (Western University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.