Abhishek Tandon

Multi up-gradation software reliability model

In software industry, up gradations are made in the software at a very brisk speed. The life of software is very short in the environment of perfect competition market therefore they have to come up with successive up gradations to survive. Features added to the software at frequent time intervals lead to complexity in the software system and add to the number of faults in the software. The developer of the software can lose on market share if it neglects the up gradation in the software but on the other hand a software company can lose its name and goodwill in the market if the functionalities added to the software leads to an increase in the number of faults of the software. To capture the effect of faults generated in the software due to add-ons at various point in time we develop a multi up gradation, multi release software reliability model. This model uniquely identifies the faults left in the software when it is in operational phase during the testing of the new code i.e, developed while adding new features to the existing software. The model developed is validated on real data sets with software which has been released in the market with new features four times.

Measuring reliability growth of software by considering fault dependency, debugging time Lag functions and irregular fluctuation

The progress of software testing is influenced by various uncertainty factors like effort expenditure, skill of test personal, testing tool, defect density, irregular state of open source project, and irregular state of software fault-report phenomena on the bug tracking system. Hence, there is an irregular fluctuation in fault detection/removal rate during testing phase. In software, the independence of failures can hardly be assumed and dependency of faults can also be considered as one of the factor for getting irregular fluctuation. In Literature, various software reliability growth models have been developed by considering fault dependency with various debugging time lag functions. But, none of the models have incorporated irregular fluctuation in their fault detection rate. Therefore, in this paper fault dependency based software reliability growth models have been developed by applying an It ∧o type Stochastic Differential Equations in order to incorporate (i) the irregular fluctuation in the fault detection process due to various uncertainty factor during testing phase and (ii) irregular state of software fault-report phenomena on the bug tracking system. The proposed stochastic differential equation based fault dependency models have been validated using (i) open source software fault count data where software fault-report phenomena on the bug tracking system keep an irregular state and (ii) a fault counting data with minor, major and critical faults. The proposed models have been compared with the existing fault dependency models. Various comparison criteria results for goodness of fit have also been presented in the paper.

Innovation diffusion of successive generations of high technology products

As most of the high-technology companies are trying to be more demand driven, technological innovation and diffusion have become important force in markets today. It critically affects the fortunes of consumers, firms, and nations. Despite research across many disciplines, many important areas still remain to be explored fully. Consumer adoption decision for multi-generation innovation is one such important research area in this field. High technology product comes in generations where a new innovation offers a significant improvement in performance or benefits over the previous generation. And, often two successive generations under the same product category compete in the market. Yet technology substitution has received little attention in the diffusion of innovation literature. Research for consumer durables has been dominated by studies of first purchase adoption which do not explicitly consider the presence of an existing technology. Only a handful of papers explicitly deal with the diffusion of technology substitution. In this work, we propose multigenerational diffusion model to study the marketing dynamics of Indian Television Market (both Black & White and Color Television). The model consider repeat-adoption-substitution diffusion framework. Comparisons with different existing multigenerational models are made. The results of this study could be advanced for forecasting new technologies of television product.

GENERAL FRAMEWORK FOR CHANGE POINT PROBLEM IN SOFTWARE RELIABILITY AND RELATED RELEASE TIME PROBLEM

Reliable software is the need of the hour especially as it is an indispensable part of our new technological world. Many SRGMs have been proposed considering the change point approach in literature. Change point is defined as the point where the fault detection rate changes, it happens due to number of reasons viz, proficiency of the testing team, nature of faults to be detected etc. In this paper we develop a generalized modeling framework incorporating change point discussing the changing nature of fault detection rate and form a related release time problem which minimizes the total cost of the software and maintains a desirable level of reliability. A numerical example is given for the release problem and the proposed models are validated on real software error data to show their goodness of fit and applicability.

A unified approach for developing two-dimensional software reliability model

Software testing can be defined as a process to detect faults in the totality and worth of developed computer software. Testing is very important in assuring the quality of the software by identifying faults in software, and possibly removing them. But testing of the software for a long time may not ensure a bug-free software and high reliability. Optimum amount of code also needs to be covered to make sure that the software is of good quality. Testing time alone may not give the correct picture of the number of faults removed in the software. Therefore, to capture the combined effect of testing time and testing coverage, we propose two-dimensional software reliability growth models. We have used Cobb–Douglas production function to develop the twodimensional model, incorporating the effect of testing time and testing coverage on the number of faults removed in the software system. A unified approach has been followed using hazard rate function for developing the models. Various continuous failure distributions are used to derive the models. The models developed are validated on real data sets and are compared to each other using various statistical tools.

An NHPP SRGM with Change Point and Multiple Releases

In an environment of intense competition, software upgrades have become the necessity for the survival in software industry. In this paper, the authors propose a discrete Software Reliability Growth Model SRGM for the software with successive releases by taking into consideration the realistic assumption that Fault Removal Rate FRR may not remain constant during the testing process, it changes due to severity of faults detected and due to change in strategies adapted by testing team and the time point where FRR changes is called the Change Point. Many researchers have developed SRGMs incorporating the concept of Change Point for single release software. The proposed model aims to present multi release software reliability modeling with change point. Discrete logistic distribution function has been used to model relationship between features enhancement and fault removal. It is helpul in developing a flexible SRGM, which is S-shaped in nature. In order to evaluate the proposed SRGM, parameter estimation is done using the real life data set for software with four releases and the goodness-of-fit of this model is analyzed.

Measuring Reliability Growth of Open Source Software by Applying Stochastic Differential Equations

This, paper presents (i) several software reliability, growth models SRGM) which tries to predict, quantitatively the failure, phenomena, in, an, Open, Source, Software, project over a period of time. Here, it is assumed that the number of failures during testing is dependent upon the number of instructions execution, (ii) in order to cater the, irregular state, of, bug-report, phenomena, on, the, bug tracking system and irregular fluctuation in terms of noise, the reliability models have been proposed by applying an Itˆo type Stochastic Differential Equations (SDE). We have demonstrated that proposed models can support management in building reliable software systems by predicting remaining bugs. We have also compared our proposed models with several existing models on the basis of different comparison criteria.

Development of software reliability growth model incorporating enhancement of features and related release policy

The software industry can be considered as the typical high technology industry where rate of innovation and knowledge creation plays a pivotal role for continued firm growth. In the last few decades it has been observed that the world of software development management has evolved rapidly due to the intensified market competition. In particular the use of feature-addition model of software products in the industry is fast becoming the commonplace. The up-gradation model can be characterized by increasing the number of features in the software that will give the firm competitive edge in the market. The up-gradation of the system is done by extending it through add-ons, interfacing with other applications, etc. Continuous up-gradation of software’s also brings complexity in the systems once it failed to work properly. In recent years, there has been a growing interest to predict the link between the rates of failure and the reliability of software. Many software reliability growth models (SRGM) have been proposed over past three decades that estimate the reliability of a software system as it undergoes changes through the removal of failure causing faults. But unfortunately most of the models did not consider anything about the increase in failure rate once an up-gradation is made on the software. The objective of this paper is to propose the software reliability growth model that incorporates the effect of enhancement of features on software during testing and debugging process. In addition, we have also discussed the related optimal release time policy that minimizes the total cost.

Reliability Growth Analysis for Multi-release Open Source Software Systems with Change Point

Open source software has now become an essential part of the business for huge segment of developers to enhance their visibility in public. Many of the open source communities are continuously upgrading the software through series of releases to improve its quality and efficiency. Here in this paper, general framework is presented to model fault removal process (FRP) for multiple releases of OSS using the concept of change point on discrete probability distribution. To validate our model, we have chosen two successful open source projects-Mozilla and Apache for its multi release failure data sets. Graphs representing goodness of fit of the proposed model have been drawn. The parameter estimates and measures of goodness of fit criteria suggest that the proposed SRGM for multi release OSS fits the actual data sets very well.

Reliability Growth Modeling for OSS: A Method Combining the Bass Model and Imperfect Debugging

In this highly competitive era of technology, for software to sustain in the market, it has to maintain its quality. Software reliability is one of the metrics to determine software quality. As very few efforts are spent on testing open source software, the reliability of open source software hugely depends on the number of users working on it after release. This study proposes new non-homogeneous Poisson process-based software reliability growth models incorporating factor of user growth in reliability growth of open source software. To represent user growth phenomenon in the proposed SRGMs, the Bass diffusion and Kenney’s models are used. The models are proposed for scenarios of both imperfect debugging and perfect debugging. Reliability analysis is carried out on real-world failure dataset (GNOME 2.0), and a parallel comparison among all SRGMs on four goodness-of-fit criteria (mean square error, coefficient of determination, predictive ratio risk, and predictive power) is performed. It is observed that SRGMs which are considered imperfect debugging outperforms its perfect counterpart which is consistent with realistic situations.