Research

Research activities, publications, and open research artifacts.

Research

AI-driven non-functional software maintenance

At The University of Auckland, I investigate how large language models and AI agents can support software maintenance activities that improve non-functional properties such as performance, maintainability, and reliability.

This research combines data-driven code-improvement patterns, retrieval-augmented generation, automated testing, and software-quality measurement to produce changes that preserve program behavior while providing measurable benefits.

Identifying and Mitigating Flaky Tests in JavaScript

My doctoral research examined flaky tests in JavaScript projects, focusing on failures caused by test execution order and differences in operating systems, Node.js versions, and browsers.

Publications

2026

SCP

JS-TOD: Detecting Order-Dependent Flaky Tests in Jest

N. Hashemi, A. Tahir, S. Rasheed, A. Shi, and R. Blagojevic

Science of Computer Programming, 2026

ICST

A Systematic Evaluation of Environmental Flakiness in JavaScript Tests

N. Hashemi, A. Tahir, S. Rasheed, A. Shi, and R. Blagojevic

IEEE International Conference on Software Testing, Verification and Validation , 2026

2025

ICST

Detecting and Evaluating Order-Dependent Flaky Tests in JavaScript

N. Hashemi, A. Tahir, S. Rasheed, A. Shi, and R. Blagojevic

IEEE International Conference on Software Testing, Verification and Validation , 2025

ICST · DS

Identifying and Mitigating Flaky Tests in JavaScript

N. Hashemi

IEEE ICST Doctoral Symposium, 2025

2023

JSS

Test flakiness’ causes, detection, impact and responses: A multivocal review

A. Tahir, S. Rasheed, J. Dietrich, N. Hashemi, and L. Zhang

Journal of Systems and Software, 2023

2022

ICSME

An Empirical Study of Flaky Tests in JavaScript

N. Hashemi, A. Tahir, and S. Rasheed

IEEE International Conference on Software Maintenance and Evolution , 2022

Research tools and datasets

Research tools

JS-TOD — Automated detection of order-dependent flaky tests in Jest through controlled test reordering and repeated execution.
js-env-sanitizer — Babel-based tooling for identifying and mitigating test failures that depend on the operating system, Node.js version, or browser.

Open datasets

FlakyJS Dataset — JavaScript projects and real-world examples of flaky tests.
OD-Test Dataset — Order-dependent tests from open-source JavaScript projects.
Env-Flaky Dataset — Environment-dependent flaky tests across JavaScript projects.