Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Aparna Kudloor Siddalingaiah
Global Academy of Technology affiliated to Visvesvaraya Technological University
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
An efficient computational framework for abstracting program slices from Java code Aparna Kudloor Siddalingaiah; Rajkumar Kulkarni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4969-4979

Abstract

Over the past decades, Java-based applications have formed the backbone of enterprise, web, and mobile software systems, often containing complex business logic and domain-specific requirements embedded in older codebases (Java SE 1.2-8). Maintaining and migrating such systems is difficult due to unstructured code, missing documentation, and outdated design artifacts. To address these challenges, the paper introduces a computationally efficient static-backward slicing framework for Java programs. The framework restructures the source code, abstracts control and data flow information, computes functional dependencies using a data flow table (DFT), and extracts precise program slices. Extracted slices are further represented using object-Z notation to formalize software specifications. Furthermore, the extracted backward slices and object-Z specifications can be leveraged with artificial intelligence (AI) and large language models (LLMs) to automatically generate human-readable software requirements. This integration also ensures structured, accurate, and minimal requirements outputs, facilitating AI-assisted software modernization. Experimental evaluations on Java programs such as automated teller machine (ATM), student grading, and quadratic root calculators exhibit significant reductions in extracted code size while preserving functional dependencies. In comparison with traditional dynamic and machine learning (ML)-based slicing approaches, the proposed framework offers improved precision, computational efficiency, and scalability, making it suitable for modernizing legacy Java systems and supporting AI-driven software engineering tasks.
Co-Authors Rajkumar Kulkarni