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    Author 1
    ORCID
    0000-0001-9275-8318
    Muhammad Inam Ul Haq
    Department of Computer Science and Bioinformatics Khushal Khan Khattak University, Karak, Pakistan
    Summary
    Muhammad Inam Ul Haq received his MS-IT from the Institute of Management Sciences, University of Peshawar, Pakistan, and his Ph.D. from Jean Monnet University, Saint-Etienne, France. He works as an Assistant Professor in the Department of Computer Science and Bioinformatics at Khushal Khan Khattak University, Karak, Pakistan. He has published several research papers in computer science and is a member of the technical review committee for several international journals. His research interests include computer vision, image processing, networks, optonumeric security, deep learning, and NLP.
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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