Can you classify bugs from screenshots of code?

2022-2023 Spring
Faculty Department of Project Supervisor: 
Faculty of Engineering and Natural Sciences
Number of Students: 

The automatic detection and classification of bugs in code blocks committed to repositories is invaluable in software development. Traditionally, this issue has been handled through semantic analysis. Recently, a new line of research has emerged suggesting that computer vision can be used for the same task; i.e. analyzing digital images and/or screenshots of code in order to determine the presence/type of bugs! (

In this project you are expected to work with existing datasets and implement a variety of deep convolutional neural network architectures for the detection and classification of software bugs.

Related Areas of Project: 
Computer Science and Engineering
Electronics Engineering

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