A Knowledge-based Automated Debugger in Learning System

TL;DR

This paper introduces Adil, a knowledge-based automated debugger designed specifically for educational purposes, enabling students to self-debug programs through pattern recognition and a library of common bugs and solutions.

Contribution

It presents a novel, user-friendly debugger tailored for learners, utilizing a knowledge-base of stereotyped code and bugs for effective self-debugging.

Findings

Successfully locates and explains logical errors in student programs.

Differentiates between correct code and bugs using pattern matching.

Provides meaningful explanations of program meaning when no errors are found.

Abstract

Currently, programming instructors continually face the problem of helping to debug students' programs. Although there currently exist a number of debuggers and debugging tools in various platforms, most of these projects or products are crafted through the needs of software maintenance, and not through the perspective of teaching of programming. Moreover, most debuggers are too general, meant for experts as well as not user-friendly. We propose a new knowledge-based automated debugger to be used as a user-friendly tool by the students to self-debug their own programs. Stereotyped code (cliche) and bugs cliche will be stored as library of plans in the knowledge-base. Recognition of correct code or bugs is based on pattern matching and constraint satisfaction. Given a syntax error-free program and its specification, this debugger called Adil (Automated Debugger in Learning system) will be able locate, pinpoint and explain logical errors of programs. If there are no errors, it will be able to explain the meaning of the program. Adil is based on the design of the Conceiver, an automated program understanding system developed at Universiti Kebangsaan Malaysia.