Testing Effort Function Research Articles

Modeling Reliability-Driven Software Release Strategy Considering Testing Effort with Fault Detection and Correction Processes: A Control Theoretic Approach

In this paper, we have introduced a software reliability growth model (SRGM) that integrates a Weibull testing effort function (TEF) within the software fault detection process (FDP) and fault correction process (FCP), effectively capturing the intricacies of software testing. Leveraging the least squared estimation method, we estimated both model parameters and the TEF, leading to a notably improved fit with the dataset and enhancing our comprehension of software reliability dynamics. Our approach also involved employing a reliability-based method to identify the optimal time for software release, further reinforcing the robustness of our model. Subsequently, we devised an optimal control model to minimize testing efforts throughout the software development life cycle. This work provides a comprehensive methodological framework for computing the total cost while ensuring debugging costs follow a learning curve pattern. Through numerical simulations involving hypothetical values and the Weibull TEF, we observed that once the software achieves the desired reliability for release, subsequent costs gradually decline to zero. In essence, both testing and future costs converge to zero post-release, reducing instantaneous expenses.

Integrating Burr type testing effort functions in logistic reliability growth model with uncertainty factor

Integrating Burr type testing effort functions in logistic reliability growth model with uncertainty factor

Software Reliability Growth Models with Exponentiated-gompertz Testing Effort and Release Time Determination

Quality is a consequential factor for the software product. During the software development at most care was taken at each step for the quality product. Development process generally embedded with several qualitative and quantitative techniques. The characteristics of final software product should reach all the standards. Reliability is a paramount element which quantifications the probability that a software product could able to work afore it authentically fails to perform its intended functionality. Software testing is paramount phase where gargantuan resources were consumed. Over around fifty percent of cost was consumed during this testing phase, that is why testing was performed in disciplined environment. Software product release time is considered to be crucial subject at which the software product testing was stopped and it could be release into market, such that the software product should have quality and reliability. In this paper we have investigated the concept of software testing effort dependent software reliability growth models by considering the exponentiated-gompertz function as testing effort function to determine the release time of the software. Thus, constructed testing effort dependent models was computed on three authentic time datasets. Parameter estimation is done through least square estimation and metrics like Mean square Error (MSE) and Absolute Error (AE) are utilized for model comparison. The proposed testing effort dependent model performance was better than the rest of the models.

Multi-release software reliability assessment: testing coverage-based approach

Software development companies continue to improve their products to stay up with the market's growing needs by adding new features and fixing previously identified bugs. Software reliability growth models (SRGMs) with testing effort function are the incredibly valuable and have been widely utilised by software engineers. Many researchers have built SRGMs that incorporate the concept testing coverage in the model building for the multi-release software system. The proposed model intends to give three different models of multi-release software reliability modelling with testing coverage function. Three models describe testing coverage by exponential function, delayed S-shaped function and logistic function respectively, with the testing effort function assumed to be Weibull in nature. Real-world data is used to estimate parameters in order to validate the proposed SRGM of four releases from Tandem Computers and the model goodness-of-fit is assessed. The results suggest that the proposed model matches the failure data effectively.

Enhanced growth model of software reliability with generalized inflection S-shaped testing-effort function

The software project manager must plan and conduct testing activities economically and efficiently. Due to the constraints, such as limited testing resources, delivery time, and cost, it isn’t easy to conclude. The software reliability growth models (SRGMs) based on testing-effort, help the project manager in the decision process. Therefore, we reckon the testing effort function (TEF) during model formulation. This paper introduces a new model for estimating the growth of software reliability. This model incorporates generalized inflection S-shaped (GISS) distribution for TEF. Here, the TEF is involved as a time-dependent failure rate function. Besides, the developed model is in perfect debugging environment. Here, we estimate the values of the unknown parameters by the least square estimation (LSE). All the performance measures are calculated using real software failure data-set and comparisons made between the proposed model and the existing models. It is noted that the performance of the proposed model prediction for the total number of faults is more trustworthy.

Imperfect Debugging in Software Systems by SRGM with TEF

Abstract: In the Proposed work we are going to assimilate two important process called TEF and imperfect debugging in software systems for analyzing FDP and FCP. Byapplying the tools called debuggers we are going to identify the failures and going to correct them in order to attain the high quality reliability. As we know, testingeffort function is predicted during this time by allocating the resources which influences considerably only for the fault identification rate and also for the correction of such faults. Additionally, new faults may be included for evaluating as the feedback. In this technique, first it is proposed to demonstrate for the inclusion of TEF and fault introduction into FDP and later develop FCP as delayedFDP with a proper effort for correction. The FCP as well FCP as paired specific models which are extracted based on the basis of types of assumptions of introducing fault introduction as well as correction effort. In addition, the optimal policy of software releasefor different criteria with examples was also presentedin this work. Keywords: FDP, FCP, TEF, Fault

Integration of Testing Effort Function into Delayed S-Shaped Software Reliability Growth Model with Imperfect Debugging — a Proposed Bokhari Model

Integration of Testing Effort Function into Delayed S-Shaped Software Reliability Growth Model with Imperfect Debugging — a Proposed Bokhari Model

An Assessment of Incorporating Log-Logistic Testing Effort Into Imperfect Debugging Delayed S-Shaped Software Reliability Growth Model

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.

SRGM using Testing-Effort Function with Uncertainty in Operating Environment

With increasing pace of technological advancement and new tech introduction in today’s word, reliability of the software has become vital. For software reliability assessment, many software reliability growth models (SRGMs) have been discussed. In literature, many of the existing SRGMs have considered uncertainty of the operating environment but very little work has included testing effort function (TEF) with uncertainty in operating environment. In this research, an SRGM incorporating Gompertz TEF has been investigated with the uncertainty of operating environment. Software testing environment is usually a controlled one with variables known to the developer, but operating environment may introduce uncontrolled and unknown variables. This model has considered a constant fault detection rate in perfect debugging environment. Further, sensitivity analysis has been done. The numerical results have been compared with existing SRGMs.

An efficient parameter optimization of software reliability growth model by using chaotic grey wolf optimization algorithm

Software reliability growth model (SRGM) with modified testing-effort function (TEF) is a function to evaluate and foresee the parameters of the data. Reliability of software is portrayed as the distinct possibility that for a predefined time, a software package will continue to run on an advance domain without frustration. SRGM utilized a few optimization procedure algorithms to advance the parameters by bifurcating them into a few stages however to upgrade the technique by using all of the parameters at the same time, the algorithm utilized is the chaotic grey wolf optimization algorithm (CGWO). CGWO is an advanced heuristic system for portraying the execution by achieving complex parameter optimization and designing application issues. Different parametric reliabilities rely upon the attributes or characteristics of the data. The parameters are predicted using the Pham–Zhang (PZ) model. Tandem computer software dataset DS1 and DS2 are used to compare the predicted parameter of SRGM obtained by Pham–Zhang (PZ) model using testing effort functions (TEFs) based on the evaluation metrics mean square error (MSE), relative error (RE) and coefficient of determination (R2). To enhance the reliability of SRGM, the parameters of SRGM estimated using TEF and enhanced using chaotic maps to improve search performance. By using the constrained benchmark functions the results of chaotic maps are obtained. Based on the chaotic graph results, the Chebyshev graph shows a good convergence rate of 78%. Overall, 86% of the results revealed an association between the choice variable and fitness criteria for CGWO. In the SRGM using CGWO, the expected result is completely mechanized and does not require any client necessity.

Optimal Allocation Based on Cost, Testing Effort and Reliability

In this paper, we investigate a software resource allocation problem. We incorporate generalized modified weibull testing effort function in software reliability growth model to get a better description of the software debugging process. The main purpose is to minimize the cost of software development when the number of remaining faults and a desired reliability objective are given. A real software failure project is demonstrated the effectiveness of proposed models.

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.

Application of Artificial Neural Network for Software Reliability Growth Modeling with Testing Effort

Background/Objectives: To design a relatively simple Software Reliability Growth Model (SRGM) with testing effort function using Artificial Neural Network approach. Methods/Statistical Analysis: The results evaluation of the proposed SRGM using Artificial Neural Network (ANN) is measured by calculating the three vital criterians namely; AIC (Akaike Information Criterian), R2 (Coefficient of determination) and RMSE (Root Mean Squared Error). Findings: Traditional time-based models may not be appropriate in some situations where the effort is varying with time. Estimating the total effort required for testing the software in the Software Development Life Cycle (SDLC) is important. Hence, a multi-layer feed-forward Artificial Neural Network (ANN) based SRGM using back propagation training is proposed in this paper by incorporating test effort. The proposed ANN based model provides consistent performance for both exponential and S-shaped growth of mean value functions witnessed in software projects. Application/Improvements: The proposed SRGM using ANN will be performed to be eminently useful for software reliability applications, since it is able to maintain its performance in all situation.

A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization

reliability is one of the key attributes to determine the quality of a software system. Finding and minimizing the remaining faults in software systems is a challenging task. Software reliability growth model (SRGM) with testing-effort function (TEF) is very helpful for software developers and has been widely accepted and applied. However, each SRGM with TEF (SRGMTEF) contains some undetermined parameters. Optimization of these parameters is a necessary task. Generally, these parameters are estimated by the Least Square Estimation (LSE) or the Maximum Likelihood Estimation (MLE). However, the software failure data may not satisfy such a distribution. We investigate the improvement and application of a swarm intelligent optimization algorithm, namely Modified Genetic Swarm Optimization algorithm, to optimize these parameters of SRGMTEF. The performance of the proposed SRGMTEF model with optimized parameters is also compared with other existing models Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The experiment results show that the proposed parameter optimization approach using Modified Genetic Swarm Optimization is very effective and flexible, and the better software reliability growth performance can be obtained based on SRGMTEF on the different software failure datasets. Also, provided comparison of ten SRGMs ( Like Goel-Okumoto Model, Delayed S-shaped Growth Model, Yamada Imperfect Debugging Models, Yamada Rayleigh Model, Inflection S-shaped Model…..etc).

Determination of the Optimal Allocation of Testing Resource for Modular Software Reliability Growth Using LINGO

It is quite natural to produce reliable software systems since the breakdown of the computer systems, which is caused by software faults, results in a tremendous loss and damage for social life. Hence, 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, in this paper four optimization problems are discussed for optimal allocation of testing resources for a modular software system. These optimization problems are formulated as nonlinear programming problems (NLPP), which incorporates log-logistic testing-effort function into inflection S-shaped software reliability growth model based on a non-homogeneous Poisson process. The NLPPs are solved using LINGO, a user-friendly software package for optimization. Finally, numerical example is given to demonstrate and to validate the performance of proposed strategies. In addition, the results of the strategies proposed in this paper are compared with [1]. It is revealed that the proposed strategies yield a gain in efficiency.

Parameter optimization of software reliability growth model with S-shaped testing-effort function using improved swarm intelligent optimization

Software reliability growth model (SRGM) with testing-effort function (TEF) is very helpful for software developers and has been widely accepted and applied. However, each SRGM with TEF (SRGMTEF) contains some undetermined parameters. Optimization of these parameters is a necessary task. Generally, these parameters are estimated by the Least Square Estimation (LSE) or the Maximum Likelihood Estimation (MLE). We found that the MLE can be used only when the software failure data to satisfy some assumptions such as to satisfy a certain distribution. However, the software failure data may not satisfy such a distribution. In this paper, we investigate the improvement and application of a swarm intelligent optimization algorithm, namely quantum particle swarm optimization (QPSO) algorithm, to optimize these parameters of SRGMTEF. The performance of the proposed SRGMTEF model with optimized parameters is also compared with other existing models. The experiment results show that the proposed parameter optimization approach using QPSO is very effective and flexible, and the better software reliability growth performance can be obtained based on SRGMTEF on the different software failure datasets.

Determining the optimal allocation of testing resource for modular software system using dynamic programming

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.

Incorporating S-shaped testing-effort functions into NHPP software reliability model with imperfect debugging

Testing-effort (TE) and imperfect debugging (ID) in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models (SRGMs). For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions (TEFs), i.e., delayed S-shaped TEF (DS-TEF) and inflected S-shaped TEF (IS-TEF), are proposed. Then these two TEFs are incorporated into various types (exponential-type, delayed S-shaped and inflected S-shaped) of non-homogeneous Poisson process (NHPP) SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new SRGMs. The experimental results show that: (i) the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs; (ii) incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs; (iii) the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.

Software reliability growth modeling with dynamic faults and release time optimization using GA and MAUT

Software testing is an essential part of software life cycle as during this period, an effort is made to improve software reliability and quality. In this phase, perfect debugging is not possible because of time lag in fault removal process or new faults may get introduced in fault removal and fault detection process. In this paper, we have studied software reliability growth model (SRGM) incorporating generalized modified Weibull (GMW) testing effort function in imperfect debugging environment with constant and time varying fault detection rates, respectively. The parameters involved in the models are estimated using maximum likelihood estimation (MLE) and non-linear least square estimation (NLLSE) methods. The performance of the proposed models is validated using mean square error (MSE), accuracy of estimation (AE), χ2 test, etc. Moreover, optimal release policy is discussed by keeping fault detection rate as a constant using both genetic algorithm (GA) and multi-attribute utility theory (MAUT). A comparison has been made with existing models reported in literature. From the empirical results, it is observed that our proposed models performed better. Further, the reliability measures are more factual in the case of time varying fault detection rate in comparison to constant fault detection rate model.

A Unified and Flexible Framework of Imperfect Debugging Dependent SRGMs with Testing-Effort

In order to overcome the limitations of debugging process, insufficient consideration of imperfect debugging and testing-effort (TE) in software reliability modeling and analysis, a software reliability growth model (SRGM) explicitly incorporating imperfect debugging and TE is developed. From the point of view of incomplete debugging and introduction of new fault, software testing process is described and a relatively unified SRGM framework is presented considering TE. The proposed framework models are fairly general models that cover a variety of the previous works on SRGM with ID and TE. Furthermore, a special SRGM incorporating an improved Logistic testing-effort function (TEF) into imperfect debugging modeling is proposed. The effectiveness and reasonableness of the proposed model are verified by published failure data set. The proposed model closer to real software testing has better descriptive and predictive power than other models