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    Author 1
    Shahida Hayat
    Department of Computer Science, University of Peshawar, Pakistan
    Summary
    Shahida Hayat is a PhD student in the Department of Computer Science at the University of Peshawar, Pakistan. She completed her MS degree in Computer Science from the same department in 2017 and received her Bachelor’s degree in Information Technology from the University of Peshawar. She is currently working as a lecturer at the Agriculture University, Pakistan. She was the winner of the All-Pakistan Competition at GIKI, Swabi, Pakistan. Her areas of specialization include Software Engineering, Artificial Intelligence, and Deep learning.
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    Free Access | Research Article | 12 November 2024
    Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling
    by
    Shahida Hayat
    ,
    Wajahat Akbar
    ,
    Tariq Hussain
    ,
    Muhammad Inam Ul Haq
    ,
    Altaf Hussian
    ,
    Irshad Khalil
    ,
    Muhammad Nawaz Khan
    ,
    Samsonova Diana
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 190-202, 2024 | DOI:10.62762/TIS.2024.751418
    Abstract
    The challenge of accurately estimating effort for software development projects is critical for project managers (PM) and researchers. A common issue they encounter is missing data values in datasets, which complicates effort estimation (EE). While several models have been introduced to address this issue, none have proven entirely effective. The Analogy-Based Effort Estimation (ABEE) model is the most widely used approach, relying on historical data for estimation. However, the common practice of deleting cases or cells with missing observations results in a reduction of statistical power and negatively impacts the performance of ABEE, leading to inefficiencies and biases. This study employ... More >
    Graphical Abstract
    Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling