Nesar Ahmad
@tmbuniv.ac.in
Professor, University Department of Statistics and Computer Applications
T. M. Bhagalpur University
RESEARCH INTERESTS
Software Reliability, Software Testing, Optimization, Medical Data Analysis, Accelerated Life Testing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Zeenat Mirza, Md Shahid Ansari, Md Shahid Iqbal, Nesar Ahmad, Nofe Alganmi, Haneen Banjar, Mohammed H. Al-Qahtani, and Sajjad Karim
MDPI AG
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.
Md Shahid Iqbal, Nesar Ahmad, Zeenat Mirza, and Sajjad Karim
Springer Science and Business Media LLC
Sajjad Karim, Md Shahid Iqbal, Nesar Ahmad, Md Shahid Ansari, Zeenat Mirza, Adnan Merdad, Saddig D. Jastaniah, and Sudhir Kumar
Elsevier BV
Nesar Ahmad, Aijaz Ahmad, and Sheikh Umar Farooq
IGI Global
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.
Md Zubair Ahmad and N. Ahmad
IEEE
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.
Seema Rani and N. Ahmad
IEEE
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.
Sheikh Umar Farooq, S.M.K. Quadri, and Nesar Ahmad
Wiley
Many empirical studies have been performed to evaluate software testing methods in past decades. However, we are still not able to generalize the results as most studies are not complete and differ significantly from one another. To contribute to the existing knowledge base of software testing methods, we performed an empirical study to evaluate 3 testing methods: (1) code reading by stepwise abstraction, (2) functional testing using equivalence partitioning and boundary value analysis, and (3) structural testing using 100% branch, multiple‐condition, loop, and relational‐operator coverage using a well‐defined and standard schema. A controlled experiment is performed with 18 subjects who applied the 3 defect detection techniques to 3 C programs in a fractional factorial experimental design to observe failures and isolate faults. The experimental results show that (1) the techniques are equally effective in terms of observing failures and finding faults, (2) the effectiveness of the techniques depends on the nature of the program, (3) all the testing techniques are equally efficient in case of failure observation, (4) the techniques are different in their efficiency in terms of fault isolation where code reading performed better than that of structural and functional testing, and (5) with respect to the fault types, all the techniques were equally effective in observing failures and isolating faults except in case of cosmetic faults where functional testing performed better than the other 2 techniques The effectiveness and efficiency of testing techniques were significantly influenced by the type of program. The results presented in this paper contribute to an empirical knowledge base of testing methods and may be helpful for the software engineers to decide the appropriate techniques in improving the software testing process.
M. G. M. Khan, N. Ahmad, and L. S. Rafi
Informa UK Limited
Abstract Reliability is a major concern in the process of software development because unreliable software can cause failure in the computer system that can be hazardous. A way to enhance the reliability of software is to detect and remove the faults during the testing phase, which begins with module testing wherein modules are tested independently to remove a substantial number of faults within a limited resource. Therefore, the available resource must be allocated among the modules in such a way that the number of faults is removed as much as possible from each of the modules to achieve higher software reliability. In this article, we discuss the problem of optimal resource allocation of the testing resource for a modular software system, which maximizes the number of faults removed subject to the conditions that the amount of testing-effort is fixed, a certain percentage of faults is to be removed and a desired level of reliability is to be achieved. The problem is formulated as a non linear programming problem (NLPP), which is modeled by the inflection S-shaped software reliability growth models (SRGM) based on a non homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing-effort functions. A solution procedure is then developed using a dynamic programming technique to solve the NLPP. Furthermore, three special cases of optimum resource allocations are also discussed. Finally, numerical examples using three sets of software failure data are presented to illustrate the procedure developed and to validate the performance of the strategies proposed in this article. Experimental results indicate that the proposed strategies may be helpful to software project managers for making the best decisions in allocating the testing resource. In addition, the results are compared with those of Kapur et al. (2004), Huang and Lyu (2005), and Jha et al. (2010) that are available in the literature to deal the similar problems addressed in this article. It reveals that the proposed dynamic programming method for the testing-resource allocation problem yields a gain in efficiency over other methods.
Javaid Iqbal, N. Ahmad, and S.M.K. Quadri
IEEE
Reliability attribute of dynamic software systems is a key to the normal operational behavior of such systems. Although the acquisition of perfect(cent percent) level of reliability for software may be practically very difficult but achieving near-perfect reliability growth levels is very much possible using reliability engineering study. Many SRGMs have been proposed including some based on Non-Homogeneous Poisson Process (NHPP). The realistic characteristics about human learning and experiential gains of new skills for better detection and correction of faults on software are being incorporated in such models. This paper incorporates two types of learning effects and a negligence factor into the SRGM with learning effect proposed by Chiu, Huang and Lee, taking advantage of the improvement proposed by Chiu, in terms of introduction of a negligent factor, in Chiu, Huang and Lee SRGM. In this paper, we simultaneously incorporate learning effect that exists in two forms: one is autonomous learning and the other is acquired learning as well as a negligence factor. The resultant model equations are subjected to the statistical analysis and the results are satisfactory.
Javaid Iqbal, S.M.K. Quadri, and N. Ahmad
IEEE
Learning and fault detection rate functions have been vigorously studied and analyzed in the modeling of software reliability growth functions. The behavior of learning and fault detection rate functions is being studied either under static assumptions or under dynamic assumptions where it can be affected by many factors, e.g., imperfect debugging, resource allocations etc. Thus, some software reliability growth functions/models model a fault detection rate function as a constant term and others take a variable (increasing) fault detection rate function. An S-shaped rate function meant to capture the learning patterns during the software testing/debugging process is being extensively employed to model a variable (increasing) fault detection rate function. The ultimate aim is to capture the realistic behavior of learning and fault detection rate functions. In this paper, we propose an NHPP based imperfect-debugging software reliability growth model with learning-factor based fault detection rate function by incorporating a learning-factor based fault detection rate function obtained from Chiu and Huang's learning model.
Javaid Iqbal, N. Ahmad, and S. M. K. Quadri
IEEE
Sheikh Umar Farooq, SMK Quadri, and Nesar Ahmad
IEEE
In this paper, we describe a controlled experiment carried out to compare three software testing methods: code reading, functional testing and structural testing. The experiment was carried out with eighteen subjects who applied three techniques to three C programs in a fractional factorial experimental design. The main results of the study are that all testing techniques are equivalent in terms of effectiveness; however the techniques differ partially in terms of efficiency.
N. Ahmad, M. G. M. Khan, and Syed Faizul Islam
IEEE
Software reliability is a key factor in software development process. Testing phase of software begins with module testing whereby, modules are tested independently to remove substantial amount of faults within a specified testing resource. Therefore, the available resource must be allocated among the modules in such a way that number of faults is removed as much as possible from each of the module to achieve higher software reliability. In this paper two optimization problem are discussed for optimal allocation of testing resources for the modular software system. These optimization problems are formulated as nonlinear programming problems (NLPP), which are modeled by a software reliability growth model based on a non-homogeneous Poisson process which incorporated Log-logistic testing-effort function. LINGO program is used to solve the optimization problems. Finally, numerical examples are given to illustrate the procedure developed in this paper. It is shown that the optimal allocation of testing-resources among software modules can improve software reliability.
Sheikh Umar Farooq, Smk Quadri, and Nesar Ahmad
IEEE
Reliability is always important in all systems but sometimes it is more important than other quality attributes. Software reliability engineering approach is focused on comprehensive techniques for developing reliable software and for proper assessment and improvement of the reliability. Reliability metrics, models and measurements form an essential part of software reliability engineering process. We should apply appropriate metrics, models and measurement techniques in SRE to produce reliable software, as no metric or model can be used in all situations. So, we should have profound knowledge of metrics, models and measurement process before applying them in SRE. In this paper, we present an in-depth analysis of all metrics, models and measurements used in software reliability.
N. Ahmad, M. G. M. Khan, L. S. Rafi, Swapan Paruya, Samarjit Kar, and Suchismita Roy
AIP
Reference [5] have proposed the log‐logistic SRGM that can capture the increasing/decreasing nature of the failure occurrence rate per fault. Therefore, in this paper, we will investigate how to incorporate the log‐logistic testing‐effort function (TEF) into inflection S‐shaped software reliability growth models based on non‐homogeneous Poisson process (NHPP). The models parameters are estimated by least square estimation (LSE) and maximum likelihood estimation (MLE) methods. The methods of data analysis and comparison criteria are presented and the experimental results from actual data applications are analyzed. Results are compared with the other existing models to show that the proposed models can give fairly better predictions. It is shown that the log‐logistic TEF is suitable for incorporating into inflection S‐shaped NHPP growth models. In addition, the proposed models are also discussed under imperfect debugging environment.
Nesar Ahmad
Inderscience Publishers
This paper discusses the optimal accelerated life test designs for Generalised Exponential (GE) distribution with log-linear model under periodic inspection and Type I censoring. For shape parameter, design and high test stresses, the accelerated life test is optimised with respect to the low test stress and the proportion of test units allocated to the low test stress. The asymptotic variance of the maximum likelihood estimator of log mean life or qth quantile at the design stress is derived as an optimality criterion with equally spaced inspection times and the optimal allocation of units for two stress levels are determined. Results show that the asymptotic variance at the design stress is insensitive to the number of inspection times and to misspecifications of guessed failure probabilities at design and high test stresses. Procedures for planning an accelerated life test, including selection of sample size, have been discussed through an example.
N. Ahmad, M.G.M. Khan, and L.S. Rafi
Emerald
PurposeThe purpose of this paper is to investigate how to incorporate the exponentiated Weibull (EW) testing‐effort function (TEF) into inflection S‐shaped software reliability growth models (SRGMs) based on non‐homogeneous Poisson process (NHPP). The aim is also to present a more flexible SRGM with imperfect debugging.Design/methodology/approachThis paper reviews the EW TEFs and discusses inflection S‐shaped SRGM with EW testing‐effort to get a better description of the software fault detection phenomenon. The SRGM parameters are estimated by weighted least square estimation (WLSE) and maximum‐likelihood estimation (MLE) methods. Furthermore, the proposed models are also discussed under imperfect debugging environment.FindingsExperimental results from three actual data applications are analyzed and compared with the other existing models. The findings reveal that the proposed SRGM has better performance and prediction capability. Results also confirm that the EW TEF is suitable for incorporating into inflection S‐shaped NHPP growth models.Research limitations/implicationsThis paper presents the WLSE results with equal weight. Future research may be carried out for unequal weights.Practical implicationsSoftware reliability modeling and estimation are a major concern in the software development process, particularly during the software testing phase, as unreliable software can cause a failure in the computer system that can be hazardous. The results obtained in this paper may facilitate the software engineers, scientists, and managers in improving the software testing process.Originality/valueThe proposed SRGM has a flexible structure and may capture features of both exponential and S‐shaped NHPP growth models for failure phenomenon.
M. G. M. Khan, N. Ahmad, and Sabiha Khan
Springer Science and Business Media LLC
N. Ahmad, M.G.M. Khan, S.M.K. Quadri, and M. Kumar
Emerald
Purpose – The purpose of this research paper is to discuss a software reliability growth model (SRGM) based on the non‐homogeneous Poisson process which incorporates the Burr type X testing‐effort function (TEF), and to determine the optimal release‐time based on cost‐reliability criteria.Design/methodology/approach – It is shown that the Burr type X TEF can be expressed as a software development/testing‐effort consumption curve. Weighted least squares estimation method is proposed to estimate the TEF parameters. The SRGM parameters are estimated by the maximum likelihood estimation method. The standard errors and confidence intervals of SRGM parameters are also obtained. Furthermore, the optimal release‐time determination based on cost‐reliability criteria has been discussed within the framework.Findings – The performance of the proposed SRGM is demonstrated by using actual data sets from three software projects. Results are compared with other traditional SRGMs to show that the proposed model has a fair...