P. K. Kapur

Contributions to hardware and software reliability

Preliminary concepts and background replacement policies with minimal repairs problems with applications to computing systems software reliability growth models based on NHPP release policies numerical computations in renewal and reliability theory.

Software Reliability Assessment with OR Applications

Software Reliability Assessment with OR Applications is a comprehensive guide to software reliability measurement, prediction, and control. It provides a thorough understanding of the field and gives solutions to the decision-making problems that concern software developers, engineers, practitioners, scientists, and researchers. Using operations research techniques, readers will learn how to solve problems under constraints such as cost, budget and schedules to achieve the highest possible quality level.Software Reliability Assessment with OR Applications is a comprehensive text on software engineering and applied statistics, state-of-the art software reliability modeling, techniques and methods for reliability assessment, and related optimization problems. It addresses various topics, including:unification methodologies in software reliability assessment;application of neural networks to software reliability assessment;software reliability growth modeling using stochastic differential equations;software release time and resource allocation problems; andoptimum component selection and reliability analysis for fault tolerant systems.Software Reliability Assessment with OR Applications is designed to cater to the needs of software engineering practitioners, developers, security or risk managers, and statisticians. It can also be used as a textbook for advanced undergraduate or postgraduate courses in software reliability, industrial engineering, and operations research and management.

A software reliability growth model for an error-removal phenomenon

The authors develop a software reliability growth model based on the non-homogeneous Poisson process, under the assumption that the detection of these errors can also cause the detection of some of the remaining errors without these errors causing any failure. Using the expected number of errors thus detected, the authors obtain the mean value function describing the failure phenomenon. Parameters of the model are estimated, and the applicability of the model is illustrated. The authors discuss the optimal release policy for such a software reliability growth model based on the cost-reliability criterion. Predictive validity of the model is discussed, and numerical results are also presented.

A Unified Approach for Developing Software Reliability Growth Models in the Presence of Imperfect Debugging and Error Generation

In this paper, we propose two general frameworks for deriving several software reliability growth models based on a non-homogeneous Poisson process (NHPP) in the presence of imperfect debugging and error generation. The proposed models are initially formulated for the case when there is no differentiation between failure observation and fault removal testing processes, and then extended for the case when there is a clear differentiation between failure observation and fault removal testing processes. During the last three decades, many software reliability growth models (SRGM) have been developed to describe software failures as a random process, and can be used to evaluate development status during testing. With SRGM, software engineers can easily measure (or forecast) the software reliability (or quality), and plot software reliability growth charts. It is not easy to select the best model from a plethora of models available. There are few SRGM in the literature of software engineering that differentiates between failure observation and fault removal processes. In real software development environments, the number of failures observed need not be the same as the number of faults removed. Due to the complexity of software systems, and an incomplete understanding of software, the testing team may not be able to remove the fault perfectly on observation of a failure, and the original fault may remain, resulting in a phenomenon known as imperfect debugging, or get replaced by another fault causing error generation. In the case of imperfect debugging, the fault content of the software remains the same; while in the case of error generation, the fault content increases as the testing progresses. Removal of observed faults may result in the introduction of new faults.

Software Reliability Growth Models

Studies in software reliability modeling started as early as early 1960s. The issues related software quality quantification and reliability measurement arose even during the time when the development of computing systems started. Since in the 1960s the cost of the computing systems were very high, use was limited to few organizations, hardware design, test and maintainability was immature, the concepts of software reliability were in infancy stage as much of the studies were concerned with the productivity and quality of the hardware systems.

Two Dimensional Multi-Release Software Reliability Modeling and Optimal Release Planning

Long-lived software systems evolve through new product releases, which involve up-gradation of previous released versions of the software in the market. But, upgrades in software lead to an increase in the fault content. Thus, for modeling the reliability growth of software with multiple releases, we must consider the failures of the upcoming upgraded release, and the failures that were not debugged in the previous release. Based on this idea, this paper proposes a mathematical modeling framework for multiple releases of software products. The proposed model takes into consideration the combined effect of schedule pressure and resource limitations using a Cobb Douglas production function in modeling the failure process using a software reliability growth model. The model developed is validated on a four release failure data set. Another major concern for the software development firms is to plan the release of the upgraded version. When different versions of the software are to be released, then the firm plans the release on the basis of testing progress of the new code, as well as the bugs reported during the operational phase of the previous version. In this paper, we formulate an optimal release planning problem which minimizes the cost of testing of the release that is to be brought into market under the constraint of removing a desired proportion of faults from the current release. The problem is illustrated using a numerical example, and is solved using a genetic algorithm.

Cost–reliability optimum release policies for a software system under penalty cost

Optimum software release policies are considered, minimizing the expected software cost simultaneously with the reliability requirement. Cost here also includes the penalty cost which is incurred by the manufacturer for not delivering the software at scheduled delivery time. The underlying software reliability growth models (SRGMs) are based on the non-homogeneous Poisson process (NHPP). Numerical results are also presented.

Modelling an imperfect debugging phenomenon in software reliability

Abstract Several Software Reliability Growth Models (SRGMs) have been developed in the literature assuming the debugging process to be perfect and thus implying that there is one-to-one correspondence between the number of failures observed and errors removed. However, in reality it is possible that the error which is supposed to have been removed may cause a failure again. It may be due to the spawning of a new error because of imperfect debugging. If such a phenomenon exists then the Software Reliability Growth is S-shaped. In this paper, we develop a model which can describe imperfect debugging process and has the inbuilt flexibility of capturing a wide class of growth curves. Earlier attempts of modelling such a process were able to capture only a particular curve. In other words, if a failure observation phenomenon is exponential then the error removal is again modelled by an exponential growth curve. Applicability of the model has been shown on several data sets obtained from different software development projects.

Software reliability growth model with error dependency

Abstract Generally, in a complex software system there may be errors, whose removal is dependent on the previously removed errors and may result in a slowing down of the removal process for a period time. This dependency can be described in different ways. In this paper, we develop an SRGM which takes care of the underlying error dependency in a software system. The SRGM has the built-in flexibility and has been tested on real software error data to show its applicability.

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.