Subhashis Chatterjee

Application of fuzzy time series in prediction of time between failures & faults in software reliability assessment

Since last seventies, various software reliability growth models (SRGMs) have been developed to estimate different measures related to quality of software like: number of remaining faults, software failure rate, reliability, cost, release time, etc. Most of the exiting SRGMs are probabilistic. These models have been developed based on various assumptions. The entire software development process is performed by human being. Also, a software can be executed in different environments. As human behavior is fuzzy and the environment is changing, the concept of fuzzy set theory is applicable in developing software reliability models. In this paper, two fuzzy time series based software reliability models have been proposed. The first one predicts the time between failures (TBFs) of software and the second one predicts the number of errors present in software. Both the models have been developed considering the software failure data as linguistic variable. Usefulness of the models has been demonstrated using real failure data.

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.

Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.

Effect of change point and imperfect debugging in software reliability and its optimal release policy

This article presents a software reliability growth model based on non-homogeneous Poisson process. The main focus of this article is to deliver a method for software reliability modelling incorporating the concept of time-dependent fault introduction and fault removal rate with change point. Also in this article, a cost model with change point has been developed. Based on the cost model optimal release policy with change point has been discussed. Maximum likelihood technique has been applied to estimate the parameters of the model. The proposed model has been validated using some real software failure data. Comparison has been made with models incorporating change point and without change point. The application of the proposed cost model has been shown using some numerical examples.

Transfer function modelling in software reliability

This paper demonstrates the applicability of transfer function model in the field of software reliability. Here a stepwise procedure for fitting a transfer function model has been described and then the prediction of remaining faults in software has been done using the built in model. Some real life data have been used for illustration purpose.

Novel Algorithms for Web Software Fault Prediction

Reliability is gaining importance with time for Web system because of the popularity of different Web-based applications, namely, different Web sites and social community networks. In order to aggrandize the reliability of a Web system, some methods are required to measure its current reliability. In recent years, some research works have been carried out on Web software error analysis and reliability predictions. In all these research works, the Web environment has been considered as a crisp one. This is not in reality. Hence, in this paper, a novel clustering algorithm and a multivariate fuzzy logic and fuzzy time series based prediction algorithm for Web software fault prediction have been developed. The proposed prediction algorithm can predict the occurrences of more than one Web error, in a single day, on a single run. Proposed methods have been validated using some real Web failure data extracted from the HTTP logs (access and error logs) of www.ismdhanbad.ac.in (the official Web site of Indian School of Mines Dhanbad, India), which were collected from the Indian School of Mines Web server. Copyright

Change point–based software reliability model under imperfect debugging with revised concept of fault dependency

A detailed study about the characteristics of different types of faults is necessary to enhance the accuracy of software reliability estimation. Over the last three decades, some software reliability growth models have been proposed considering the possibility of existence of two types of faults in a software: (1) independent and (2) dependent faults. In these software reliability growth models, it is considered that the removal of a leading fault or independent fault causes detection of corresponding dependent faults. In practical, it is noticed that some dependent faults are possible in a software which are removed during the removal of other faults. Moreover, dependent faults may have different characteristics, which cannot be ignored. Considering these facts, a detailed study about the different characteristics of both dependent and independent faults has been performed, and based on this study, dependent faults have been categorized into different categories. Furthermore, a new software reliability growth model has been proposed with revised concept of fault dependency under imperfect debugging by introducing the fault removal proportionality. In addition, the effect of change point on model’s parameters due to different environmental factors has been considered. The fault reduction factor is considered as a proportionality function. Experimental results establish the fact that the performance of the proposed model is better with respect to estimated and predicted cumulative number of faults on some real software failure datasets.

An Ideal Software Release Policy for an Improved Software Reliability Growth Model Incorporating Imperfect Debugging with Fault Removal Efficiency and Change Point

This paper presents a general software reliability growth model (SRGM) based on non-homogeneous Poisson process (NHPP) and optimal software release policy with cost and reliability criteria. The main motive of this study is to develop a software release time decision model considering maintenance cost and warranty cost under fuzzy environment. In previous studies, maintenance cost has been defined either in terms of warranty cost or fault debugging cost. In reality, maintenance cost includes the cost of free patches, updates, technical support and future enhancement. Also, it is possible that maintenance process causes the removal of software faults in the operational phase including the faults which occur outside the warranty period or warranty definition. In other words, warranty action may be included the maintenance action, but not the converse. Considering this fact, maintenance cost and warranty cost are defined separately in the proposed study. Initially, an SRGM has been proposed with the revised concept of imperfect debugging phenomenon considering fault removal efficiency (FRE). Furthermore, the effect of changes in various environmental factors on models parameters has been taken into account. Numerical examples based on real software failure data sets have been given to analyze the performance of the proposed models.

Software reliability modeling with different type of faults incorporating both imperfect debugging and change point

In past four decades, many nonhomogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) have been proposed to measure and assess the reliability growth of software. During the testing process, the faults which causes failure are detected and removed. One common assumption of many traditional SRGMs is that the fault removal rate is constant. In practical, the fault removal rate increases with time as learning and maturity of software engineer increases. Hence, time variant fault removal rate has been considered in this study. A complex software system may contain different category of faults. Some of faults can be easily detected and removed and some of faults required more effort to be detected and removed. Therefore, in this article, a NHPP based SRGM has been proposed which incorporates mainly two type of faults, major and minor. The concepts of imperfect debugging and change point have also been incorporated in the proposed SRGM. The parameters of the proposed SRGM is estimated using Statistical Package for Social Sciences (SPSS) software and validation of the proposed SRGM has been done using real life data set.

Best Subset Selection of ARMA and ARIMA Models for Software Reliability Estimation

Abstract Many software reliability models have been proposed during last three decades. Almost all these software reliability models have been developed based on many assumptions. One approach for development of assumption free software reliability model is time series. In this paper ARMA and ARIMA models have been developed for predicting software reliability. The proposed models have been validated using some real software failure data.