Morteza Zakeri Nasrabadi

Orcid: 0000-0003-4289-0606

Affiliations:
  • Amirkabir University of Technology, Department of Computer Engineering, Tehran, Iran
  • Iran University of Science and Technology, School of Computer Engineering, Tehran, Iran (PhD)


According to our database1, Morteza Zakeri Nasrabadi authored at least 22 papers between 2018 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
A Systematic Literature Review on Design for Testability Approaches in Software Systems.
ACM Comput. Surv., September, 2026

Enhancing software quality attributes through multi-dimensional refactoring at source-level.
Sci. Comput. Program., 2026

QualCode: A Data-Driven Framework for Predicting Software Maintainability Based on ISO/IEC 25010.
Sci. Comput. Program., 2026

2025
A systematic literature review on transformation for testability techniques in software systems.
Inf. Softw. Technol., 2025

Testability-driven development: An improvement to the TDD efficiency.
Comput. Stand. Interfaces, 2025

2024
Dynamic domain testing with multi-agent Markov chain Monte Carlo method.
Soft Comput., July, 2024

Natural language requirements testability measurement based on requirement smells.
Neural Comput. Appl., July, 2024

Supporting single responsibility through automated extract method refactoring.
Empir. Softw. Eng., February, 2024

Measuring and improving software testability at the design level.
Inf. Softw. Technol., 2024

Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT.
Comput. Stand. Interfaces, 2024

2023
A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges.
J. Syst. Softw., October, 2023

Method name recommendation based on source code metrics.
J. Comput. Lang., January, 2023

A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms.
ACM Comput. Surv., 2023

Mitigating Backdoors within Deep Neural Networks in Data-limited Configuration.
CoRR, 2023

2022
An automated extract method refactoring approach to correct the long method code smell.
J. Syst. Softw., 2022

Learning to predict test effectiveness.
Int. J. Intell. Syst., 2022

Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 8 August 2022.
Int. J. Intell. Syst., 2022

An ensemble meta-estimator to predict source code testability.
Appl. Soft Comput., 2022

2021
Format-aware learn&fuzz: deep test data generation for efficient fuzzing.
Neural Comput. Appl., 2021

A comprehensive survey on non-invasive wearable bladder volume monitoring systems.
Medical Biol. Eng. Comput., 2021

Learning to Predict Software Testability.
Proceedings of the 26th International Computer Conference, Computer Society of Iran, 2021

2018
Neural Fuzzing: A Neural Approach to Generate Test Data for File Format Fuzzing.
CoRR, 2018


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