scholarly journals Optimal Testing Effort Control for Modular Software System Incorporating The Concept of Independent and Dependent Faults: A Control Theoretic Approach

2012 ◽  
Vol 2 (2) ◽  
pp. 129-137
Author(s):  
Kuldeep CHAUDHARY ◽  
P. C. JHA
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Budget Constraint ◽  
Software System ◽  
Reliability Growth ◽  
Theoretic Approach ◽  
Testing Effort ◽  
Modular Software ◽  
Software Reliability Growth ◽  
The Cost

In this paper, we discuss modular software system for Software Reliability Growth Models using testing effort and study the optimal testing effort intensity for each module. The main goal is to minimize the cost of software development when budget constraint on testing expenditure is given. We discuss the evolution of faults removal dynamics in incorporating the idea of leading /independent and dependent faults in modular software system under the assumption that testing of each of the modulus is done independently. The problem is formulated as an optimal control problem and the solution to the proposed problem has been obtained by using Pontryagin Maximum Principle.

scholarly journals Bivariate Software Reliability Growth Models under Budget Constraint for Development Management

2019 ◽  
Vol 5 (1) ◽  
pp. 56-65
Author(s):  
Yuka Minamino ◽  
Shinji Inoue ◽  
Shigeru Yamada
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Budget Constraint ◽  
Testing Time ◽  
Reliability Growth ◽  
Constant Elasticity ◽  
Testing Effort ◽  
Software Reliability Growth Models ◽  
Substitution Type ◽  
Software Reliability Growth

Software reliability growth is observed by investing not only the testing-time but also the testing-effort in the testing-phase of software development process. If the testing-time (testing-effort) is reduced to some extent, it is possible to observe the software reliability growth by investing the amount of testing-effort (testing-time) which can compensate the insufficiency of the testing-time (testing-effort). However, most of the existing software reliability growth models (SRGMs) are constructed as univariate models and the substitutability between the testing-time and testing-effort is not considered. Additionally, it is necessary to remove many faults efficiently within the budget. In this paper, we develop bivariate Weibull type SRGMs under budget constraint based on the Cobb-Douglas type and CES (constant elasticity of substitution) type testing-time functions. Simultaneously, we evaluate the substitutability between the testing-time and testing-effort factors which are software reliability growth factors. Finally, we conduct the sensitivity analysis and show numerical examples by using actual data sets.

A SOFTWARE RELIABILITY GROWTH MODEL WITH TESTING EFFORT DEPENDENT LEARNING FUNCTION FOR DISTRIBUTED SYSTEMS

2004 ◽  
Vol 11 (04) ◽  
pp. 365-377 ◽  
Author(s):  
P. K. KAPUR ◽  
D. N. GOSWAMI ◽  
AMIT GUPTA
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Software Process Improvement ◽  
Software System ◽  
Reliability Growth ◽  
Development Environment ◽  
Testing Effort ◽  
Growth Phenomenon ◽  
Software Reliability Growth ◽  
Fault Removal

Effective software process improvement will not start until management insists that product development work be planned and properly managed. This becomes even more challenging in an increasing number of major system developments made up from distributed sub-system software projects. These sub-systems are integrated and validated to provide the final system and product release. The need is growing to estimate, risk assess, plan and manage the development of these distributed sub-systems and the final full system release. In this paper, an attempt has been made to model the software reliability growth phenomenon with testing effort in a distributed development environment. Proposed Non Homogeneous Poisson Process (NHPP) based model assumes that the software system consists of a finite number of reused and newly developed sub-systems. The reused sub-systems do not consider the effect of severity of the faults on the software reliability growth phenomenon because they stabilize over a period of time i.e., the growth is uniform whereas, the newly developed sub-system do consider that. Fault removal phenomenon for reused and newly developed sub-systems have been modeled separately and is summed up to get the total fault removal phenomenon of the software system. The applicability of our model is shown by validating it on software failure data sets obtained from different real software development projects. The comparisons with established models in terms of goodness of fit, the Akaike Information Criterion (AIC), Mean of Squared Errors (MSE) have been presented.

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

2021 ◽  
Vol 9 (3) ◽  
pp. 23-41
Author(s):  
Nesar Ahmad ◽  
Aijaz Ahmad ◽  
Sheikh Umar Farooq
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Least Square ◽  
Model Parameters ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Testing Effort ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Effort Function

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.

CONSIDERING TESTING EFFICIENCY AND TESTING RESOURCE CONSUMPTION VARIATIONS IN ESTIMATING SOFTWARE RELIABILITY

2008 ◽  
Vol 15 (02) ◽  
pp. 77-91 ◽  
Author(s):  
ANSHU GUPTA ◽  
REECHA KAPUR ◽  
P. C. JHA
Keyword(s):  
Software Testing ◽  
Software Reliability ◽  
Growth Models ◽  
Real Life ◽  
Resource Consumption ◽  
Experimental Result ◽  
Reliability Growth ◽  
Testing Effort ◽  
Software Reliability Growth ◽  
Testing Efficiency

Advances in software technologies have promoted the growth of computer-related applications to a great extent. Building quality in terms of reliability of the software has become one of the main issues for software developers. Software testing is necessary to build highly reliable software. Monitoring and controlling the resource utilization, measuring and controlling the progress of testing, efficiency of testing and debugging personals and reliability growth are important for effective management the testing phase and meeting the quality objectives. Over the past 35 years many Software reliability growth models (SRGM) are proposed to accomplish the above-mentioned activities related to the software testing. From the literature it appears that most of the SRGM do not account the changes in the testing effort consumption. During the testing process especially in the beginning and towards the end of the testing frequent changes are observed in testing resource consumption due to changes in testing strategy, team constitution, schedule pressures etc. Apart from this testing efficiency plays a major role determining the progress of the testing process. In this paper we incorporate the important concept of testing resource consumption variations for Weibull type testing effort functions and testing efficiency in software reliability growth modeling. The performance of the proposed models is demonstrated through two real life data sets existing in literature. The experimental result shows fairly accurate estimating capabilities of the proposed models.

Optimization Problems for a Modular Software with Faults of Different Severity

2016 ◽  
Vol 23 (03) ◽  
pp. 1640004 ◽  
Author(s):  
P. K. Kapur ◽  
Nitin Sachdeva
Keyword(s):  
Software Reliability ◽  
Goodness Of Fit ◽  
Optimization Problems ◽  
Software System ◽  
Reliability Growth ◽  
Data Set ◽  
The Past ◽  
Modular Software ◽  
Testing Stage ◽  
Software Reliability Growth

For the past several decades, reliability has been presumed as the most important characteristic of any complex software system. Software developer’s assurance of high quality and reliable software is driven on the basis of efficient reliability assessment. Since 1970’s an unprecedented growth has been observed in the area of software reliability growth modeling. Software reliability growth models (SRGM) are linked to the testing stage of software development and provides for insights into ways to improve reliability of the system and optimal time to release the software. Several SRGMs have been proposed in the literature to model fault identification/removal phenomenon based on time; search for more efficient and accurate models that can fit greater number of reliability growth curves is endless. Categorization of faults lying in the software has been widely studied as well. Also, efforts have been made in the past to understand reliability issues concerned with modular software system. In this paper we examine three different types of faults lying in complex software and study their behavior in an N-module software system. In order to attain two fold objectives of maximizing reliability of such a system and minimizing overall debugging cost, we propose two related optimization models. The inbuilt model flexibility takes care of different environment. The model is validated by real life software failure data set to show its goodness of fit and applicability.

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