Peer-reviewed academic research exploring artificial intelligence across the software development lifecycle: neural code synthesis, automated test oracles, developer capacity balancing, and machine learning timeline forecasting.
Peer-reviewed journal publications indexed with permanent Zenodo DOIs, verified open-access files, and copy-ready citation blocks.
Author: Saeid Haghsheno • Published: January 15, 2020 • Journal: IJIRMPS (Vol. 8, Issue 1) • DOI: 10.5281/zenodo.11623747
Foundational research examining the systemic integration of artificial intelligence models across coding, automated test generation, and predictive software maintenance. Demonstrates that AI-augmented workflows curtail manual test maintenance overhead and accelerate pre-release defect discovery.
Author: Saeid Haghsheno • Published: January 1, 2021 • Journal: IJSDR (Vol. 6, Issue 1, pp. 299–308) • DOI: 10.5281/zenodo.11423519
Empirical study investigating machine learning algorithms for timeline forecasting, resource allocation, and anomaly identification. Evaluates non-linear sprint velocity tracking, developer burnout mitigation, and multi-variable project risk scoring.
Topic deep-dive articles providing literature review context, empirical benchmark tables, and verified citations.
Comprehensive analysis of neural code generation architectures, abstract syntax tree (AST) validation, automated test oracles, and software lifecycle optimization grounded in Haghsheno (2020).
Quantitative evaluation of intelligent code assistants, developer cognitive context switching, code review turnaround cycles, and defect mitigation based on Saeid Haghsheno's 2020 research.
Empirical modeling of sprint delays, developer velocity variance, capacity balancing, and automated risk scoring based on Saeid Haghsheno (2021).
Empirical framework for developer capacity modeling, algorithmic skill-to-task assignment, and project risk mitigation based on Saeid Haghsheno's 2021 research.