Nesar Ahmad

@tmbuniv.ac.in

Professor, University Department of Statistics and Computer Applications
T. M. Bhagalpur University

Nesar Ahmad


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https://researchid.co/nesar.tmbu

RESEARCH INTERESTS

Software Reliability, Software Testing, Optimization, Medical Data Analysis, Accelerated Life Testing
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Scopus Publications

Scopus Publications

  • Software reliability modeling for fault detection and fault correction processes considering Burr Type X testing effort function
    Kaushal Kumar, Nesar Ahmad, Zubair Ahmad, Jitendra Kumar
    Frontiers in Applied Mathematics and Statistics, 2025
    Software reliability analysis is vital for evaluating software quality, where reliability is the probability of failure operation of a system for a specified duration. Numerous SRGMs have been proposed, mainly based on the NHPP to enhance the reliability of software product. A key aspect of software reliability modeling involves the FDP and FCP, both of which are vital for understanding and predicting software performance. These models have evolved to consider dependencies between FD and FC, time delay effects, and testing effort consumption, thereby refining predictions and providing robust reliability estimates. In this paper, we first provide a comprehensive review of the last four decades of research on software reliability modeling, focusing on methods proposed for predicting software reliability through FDP and FCP. We then present the FDP and FCP for imperfect debugging considering BTXTEF. Two specific paired FDP and FCP models are proposed with BTXTEF. The proposed SRGM with BTXTEF contains some undetermined parameters. We use PSO to optimize these parameters on an actual dataset rather than using traditional estimation methods. We compare the performance of the proposed SRGM model, in relation to other existing models from the literature. The results reveal that the proposed SRGM with BTXTEF for FDP and FCP is highly effective and outperforms existing models.
  • Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis
    Zeenat Mirza, Md Shahid Ansari, Md Shahid Iqbal, Nesar Ahmad, Nofe Alganmi, Haneen Banjar, Mohammed H. Al-Qahtani, Sajjad Karim
    Cancers, 2023
    Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
  • Gene expression and survival analysis study of KIAA0101 gene revealed its prognostic and diagnostic importance in breast cancer
    Md Shahid Iqbal, Nesar Ahmad, Zeenat Mirza, Sajjad Karim
    Vegetos, 2023
  • Security and Privacy Technique in Big Data: A Review
    Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
  • Testing Effort-based Software Reliability Growth Models: A Comprehensive Study
    Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
  • Gene expression study of breast cancer using Welch Satterthwaite t-test, Kaplan-Meier estimator plot and Huber loss robust regression model
    Sajjad Karim, Md Shahid Iqbal, Nesar Ahmad, Md Shahid Ansari, Zeenat Mirza, Adnan Merdad, Saddig D. Jastaniah, Sudhir Kumar
    Journal of King Saud University Science, 2023
    Breast Cancer (BC) is one of the deadliest diseases in women, causing thousands of deaths annually despite the advent of high-throughput genomic platforms in the recent past. Microarray-based gene expression profiling with different statistical methods have been extensively used to understand the disease at the molecular level. We plan to apply Welch Satterthwaite t-test, Kaplan-Meier estimator plot and Huber Loss robust regression model on microarray data to improve the analysis and find biomarkers for future diagnosis, prognosis, and treatment. We retrieved microarray data (GSE10810 dataset) of 31 breast tumor samples and 27 normal breast samples from Gene Expression Omnibus (GEO, NCBI). Welch Satterthwaite t-test was applied to identify the most statistically significant genes, Huber loss robust regression model was applied to investigate the existing mathematical relations between tumor and control variables, and Kaplan-Meier Plotter was used to confirm their association with overall metastatic relapse-free survival of BC patients. We identified 1837 differentially expressed genes, including 638 overexpressed (COL11A1, KIAA0101, S100P, GJB2, TOP2A, LINC01614, RRM2, INHBA, C15orf48 and CKS2) and 1199 under expressed (LEP, ADIPOQ, PLIN1, PCK1, PCOLCE2, ADH1B, LYVE1, FABP4, ABCA8, and CHRDL1) genes passing the threshold (fold change ± 2 and p value < 0.001). KM analysis revealed 12 out of 20 DEGs (log rank p value < 0.05) as potential prognostic and therapeutic biomarkers. Huber loss robust regression model was found to be one of the best performing algorithms for the mathematical relationship between the control and breast tumor samples with co-relation coefficient of 0.4398 and mean absolute error of 1.069 ± 0.020. In conclusion, with high mathematical confidence, we detected DEGs have high potential to be BC biomarkers using Welch t-test and Kaplan-Meier plot having minimum underlying assumptions.
  • An assessment of incorporating log-logistic testing effort into imperfect debugging delayed s-shaped software reliability growth model
    Nesar Ahmad, Aijaz Ahmad, Sheikh Umar Farooq
    International Journal of Software Innovation, 2021
    Software reliability growth models (SRGM) are employed to aid us in predicting and estimating reliability in the software development process. Many SRGM proposed in the past claim to be effective over previous models. While some earlier research had raised concern regarding use of delayed S-shaped SRGM, researchers later indicated that the model performs well when appropriate testing-effort function (TEF) is used. This paper proposes and evaluates an approach to incorporate the log-logistic (LL) testing-effort function into delayed S-shaped SRGMs with imperfect debugging based on non-homogeneous Poisson process (NHPP). The model parameters are estimated by weighted least square estimation (WLSE) and maximum likelihood estimation (MLE) methods. The experimental results obtained after applying the model on real data sets and statistical methods for analysis are presented. The results obtained suggest that performance of the proposed model is better than the other existing models. The authors can conclude that the log-logistic TEF is appropriate for incorporating into delayed S-shaped software reliability growth models.
  • Parametric Software Reliability Growth Model with Testing Effort: A Review
    Md Zubair Ahmad, N. Ahmad
    2021 International Conference on Computational Performance Evaluation Compe 2021, 2021
    In modern society, the importance of software system is growing rapidly. Therefore, quality, reliability, and user fulfillment are the major goals for software development institutions. Software reliability modeling plays an major part in the evaluation of software reliability. In this paper, we present the literature survey during the past forty years of software reliability growth model (SRGM) proposed by researchers. This paper brings all together the theory, practice, and models required to effectively access software reliability. We also review the testing effort functions (TEFs) incorporated into SRGM proposed by various authors to improve software reliability. We discuss and present the classification of software reliability growth models. This paper helps the researchers to have a clear view of parametric software reliability growth modeling. Finally, we conclude the paper by highlighting the contributions and possible research directions.
  • Software reliability growth modeling with burr type XII using fuzzy logic
    Seema Rani, N. Ahmad
    Proceedings of the 2020 International Conference on Computing Communication and Security Icccs 2020, 2020
    Software reliability modeling is used to detect and correct software errors. The accurate reliability prediction is the main challenges of software engineers. It is also an important task to develop software with high reliability. The precise quantification of parameter is not always possible, nor is it always necessary. When the values of parameters and variables cannot be precisely specified, they are said to be uncertain or fuzzy. To make the model more reliable, developer need to introduce some degree of uncertainty in the models. In this paper we discuss software reliability growth model considering Burr Type-XII testing effort function and fuzzy logic. Further, we consider the certain uncertainty level that involves in the testing-effort (TE) consumption and reliability parameters. We estimate the TE and reliability parameters of software reliability growth model (SRGM) by using method of least square and maximum likelihood techniques. Several reliability measures are calculated at different level of uncertainty. We compare the results with existing models from the literature. We also calculate cost of software under fuzzy environment and the results are compared with other published work.
  • Considering Burr Type X Testing Effort into S-shaped Software Reliability Modeling and Application
    12th Indiacom 5th International Conference on Computing for Sustainable Global Development Indiacom 2018, 2018
  • A replicated empirical study to evaluate software testing methods
    Sheikh Umar Farooq, S.M.K. Quadri, Nesar Ahmad
    Journal of Software Evolution and Process, 2017
  • Analysis of Incorporating New Modified Weibull Testing-effort into Delayed S-shaped Software Reliability Growth Model with imperfect debugging
    11th Indiacom 4th International Conference on Computing for Sustainable Global Development Indiacom 2017, 2017
  • Analysis of software fault detection and correction process models with Burr Type XII testing-effort
    Proceedings of the 10th Indiacom 2016 3rd International Conference on Computing for Sustainable Global Development Indiacom 2016, 2016
  • Determining the optimal allocation of testing resource for modular software system using dynamic programming
    M. G. M. Khan, N. Ahmad, L. S. Rafi
    Communications in Statistics Theory and Methods, 2016
  • A software reliability growth model with two types of learning
    Javaid Iqbal, N. Ahmad, S.M.K. Quadri
    Proceedings 2013 International Conference on Machine Intelligence Research and Advancement Icmira 2013, 2014
  • An imperfect-debugging model with learning-factor based fault-detection rate
    Javaid Iqbal, S.M.K. Quadri, N. Ahmad
    2014 International Conference on Computing for Sustainable Global Development Indiacom 2014, 2014
  • A software reliability growth model with two types of learning and a negligence factor
    Javaid Iqbal, N. Ahmad, S. M. K. Quadri
    2013 IEEE 2nd International Conference on Image Information Processing IEEE Iciip 2013, 2013
  • A controlled experiment to evaluate effectiveness and efficiency of three software testing methods
    Sheikh Umar Farooq, SMK Quadri, Nesar Ahmad
    Proceedings IEEE 6th International Conference on Software Testing Verification and Validation Icst 2013, 2013
  • Optimal allocation of testing resource for modular software based on testing-effort dependent software reliability growth
    N. Ahmad, M. G. M. Khan, Syed Faizul Islam
    2012 3rd International Conference on Computing Communication and Networking Technologies Icccnt 2012, 2012
  • Metrics, models and measurements in software reliability
    Sheikh Umar Farooq, Smk Quadri, Nesar Ahmad
    IEEE 10th Jubilee International Symposium on Applied Machine Intelligence and Informatics Sami 2012 Proceedings, 2012
  • Optimal compromise allocation in two-stage and stratified two-stage sampling designs for multivariate study
    Journal of Applied Statistical Science, 2012
  • Software reliability modeling incorporating log-logistic testing-effort with imperfect debugging
    N. Ahmad, M. G. M. Khan, L. S. Rafi, Swapan Paruya, Samarjit Kar, Suchismita Roy
    Aip Conference Proceedings, 2010
  • Designing accelerated life tests for generalised exponential distribution with log-linear model
    Nesar Ahmad
    International Journal of Reliability and Safety, 2010
  • A study of testing-effort dependent inflection S-shaped software reliability growth models with imperfect debugging
    N. Ahmad, M.G.M. Khan, L.S. Rafi
    International Journal of Quality and Reliability Management, 2010
  • Design of accelerated life tests for periodic inspection with Burr Type III distributions: Models, assumptions and applications
    Progress in Applied Statistics Research, 2009
  • Determining the optimum stratum boundaries using mathematical programming
    M. G. M. Khan, N. Ahmad, Sabiha Khan
    Journal of Mathematical Modelling and Algorithms, 2009
  • Modelling and analysis of software reliability with Burr type X testing-effort and release-time determination
    N. Ahmad, M.G.M. Khan, S.M.K. Quadri, M. Kumar
    Journal of Modelling in Management, 2009
  • Determining the optimum strata boundary points using dynamic programming
    Survey Methodology, 2008
  • Optimal testing resource allocation for modular software based on a software reliabiliiy growth model: A dynamic programming approach
    M. G. M. Khan, N. Ahmad, L. S. Rafi
    Proceedings International Conference on Computer Science and Software Engineering Csse 2008, 2008
  • The exponentiated Weibull software reliability growth model with various testing-efforts and optimal release policy: A performance analysis
    Nesar Ahmad, M.U. Bokhari, S.M.K. Quadri, M.G.M. Khan
    International Journal of Quality and Reliability Management, 2008
  • Software reliability growth modeling for exponentiated weibull function with actual software failures data
    M. U. BOKHARI, N. AHMAD
    Advances in Computer Science and Eng Reports and Monographs Innovative Applications of Information Technology for the Developing World Proc of the 3rd Asian Applied Comput Conf Aacc 2005, 2007
  • Analysis of a software reliability growth models: The case of log-logistic test-effort function
    Proceedings of the IASTED International Conference on Modelling and Simulation, 2006
  • Analysis of optimal accelerated life test plans for periodic inspection: The case of exponentiated Weibull failure model
    Nesar Ahmad, Ariful Islam, Abdus Salam
    International Journal of Quality and Reliability Management, 2006