activity
20222024
most citedA systematic literature review on the code smells datasets and validation mechanisms

43 citations · 75 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2024

Natural Language Requirements Testability Measurement Based on Requirement Smells

Morteza Zakeri-Nasrabadi, Saeed Parsa

Requirements form the basis for defining software systems' obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perfo…

cs.LG2023

Mitigating Backdoors within Deep Neural Networks in Data-limited Configuration

Soroush Hashemifar, Saeed Parsa, Morteza Zakeri-Nasrabadi

As the capacity of deep neural networks (DNNs) increases, their need for huge amounts of data significantly grows. A common practice is to outsource the training process or collect…

cs.SE202343 cited

A systematic literature review on the code smells datasets and validation mechanisms

Morteza Zakeri-Nasrabadi, Saeed Parsa, Ehsan Esmaili +1

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a d…

cs.SE202217 cited

An ensemble meta-estimator to predict source code testability

Morteza Zakeri-Nasrabadi, Saeed Parsa

Unlike most other software quality attributes, testability cannot be evaluated solely based on the characteristics of the source code. The effectiveness of the test suite and the b…

cs.SE202215 cited

Learning to predict test effectiveness

Morteza Zakeri-Nasrabadi, Saeed Parsa

The high cost of the test can be dramatically reduced, provided that the coverability as an inherent feature of the code under test is predictable. This article offers a machine le…