Mahshid Helali Moghadam, Researcher

Mahshid Helali Moghadam holds a PhD degree in computer science from Mälardalen university. Her PhD research entitled "Intelligence-Driven Software Performance Assurance" focused on the intersection of applied machine learning and software engineering with a focus on software performance assurance.

She obtained her Licentiate degree in computer science from Mälardalen University in 2020. She received her M.Sc. degree in artificial intelligence and machine learning and her B.Sc. degree in software engineering from Iran University of Science and Technology (IUST), Tehran, Iran in 2011 and 2008 respectively.

Her research directions are based on the use of machine learning and computational intelligence techniques to build autonomous software agents for robustness testing of AI and non-AI software systems, runtime software performance control, fault diagnosis, and performance anomaly detection. Her research interests comprise intelligent software engineering and software engineering for intelligent systems, machine learning, and data science. She has been working in various European research projects and is currently a project leader as a part of Smart Industrial Automation unit at RISE in Västerås.



Research Interests:  Intelligent Software Engineering & Software Engineering for Intelligent Systems (Software Quality Assurance, Machine Learning & Autonomous systems)

 Recent Projects:

InSecTT-Intelligent Secure Trustable Things

AIDoRt - AI-augmented automation for efficient DevOps and the continuous development At RunTime in cyber-physical systems

DAIS-Distributed Artificial Intelligent Systems

IVVES - Industrial-grade Verification and Validation of Evolving Systems

ITS ESS-H Industrial Graduate School in Reliable Embedded Sensor Systems

TESTOMAT Project - The Next Level of Test Automation

XIVT - eXcellence in Variant Testing


[Show all publications]

Latest publications:

Intelligence-Driven Software Performance Assurance (May 2022)
Mahshid Helali Moghadam

Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing (Mar 2022)
Mahshid Helali Moghadam, Markus Borg , Mehrdad Saadatmand , Seyed Jalaleddin Mousavirad , Markus Bohlin, Björn Lisper

Efficient and Effective Generation of Test Cases for Pedestrian Detection – Search-based Software Testing of Baidu Apollo in SVL (Aug 2021)
Hamid Ebadi , Mahshid Helali Moghadam, Markus Borg , Gregory Gay , Afonso Fontes , Kasper Socha
The Third IEEE International Conference on Artificial Intelligence Testing (AITest'21)

A Population-Based Automatic Clustering Algorithm for Image Segmentation (Jul 2021)
Seyed Jalaleddin Mousavirad , Gerald Schaefer , Mahshid Helali Moghadam, Mehrdad Saadatmand
The Genetic and Evolutionary Computation Conference Companion 2021 (GECCOC 2021)

Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent (Jul 2021)
Mahshid Helali Moghadam, Golrokh Hamidi , Markus Borg , Mehrdad Saadatmand , Markus Bohlin, Björn Lisper, Pasqualina Potena
2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021)

Deeper at the SBST 2021 Tool Competition: ADAS Testing Using Multi-Objective Search (May 2021)
Mahshid Helali Moghadam, Markus Borg , Seyed Jalaleddin Mousavirad
2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing, Co-located with ICSE 2021 (SBST 2021)