Software Reliability and manintainability

In software development life cycle, the most important activity is software maintenance, in order to get a reliable and quality product. Huge amount of time, cost and effort is involved in it. Maintenance of software encompasses various activities like
prediction, detection, prevention and correction of fault. Due to refined and multifaceted applications, clustered architecture, artificial intelligence and commercial hardware are in use. Hence, in this research work a review is conducted in the field of software fault detection and correction. There are a lot of software reliability growth models and techniques which help in software fault detection and correction, nevertheless, the room for more models and processes is vacant to detect and correct faults.

In this author's opinion, following are the most important software quality attributes - Reliability, Usability, Performance and Availability. As said wisely, "Things that count most, can not be counted mostly." This paper tries to suggest how 'Reliability' can be measured from end user's perspective.
For any software system, end user is the most important actor. Measuring ‘Reliability’ is a way to quantify the usefulness of the software for and end user. Therefore, measuring Reliability from an end user's perspective is very important. Unfortunately, there doesn't seem to be much discussion about how much reliable the software is for end user

In this paper using the main feature of our proposed Model in its inflection point, we propose a software reliability growth model, which relatively early in the testing and debugging phase, provides accurate parameters estimation, gives a very good failure behavior prediction and enable software developers to predict when to conclude testing, release the software and avoid over testing in order to cut the cost during the development and the maintenance of the software. Two real world experimental data previously analyzed have been used to compare our proposed Early Estimation Logistic Model effectiveness with several pre-existing models.

Analyzing the reliability of a software can be done at various phases during the development of engineering software. Software reliability growth models (SRGMs) assess, predict, and controlthe software reliability based on data obtained from testing phase.This paper gives a literaturereview of the first and wellknownJelinski and Moranda(J-M) (1972)SRGM.Also a modification to Jelinski and Morandamodel is given, Jelinski and Moranda and Schick and Wolverton (S-W) (1978)SRGMsare two special cases of our new suggested general SRGM. Our proposed general SRGMalong with our Survey will open doors for much more useful researches to be done in the field of reliability modeling.

Number of software Reliability growth models has been proposed in the literature. A mathematical technique which describes the software testing phenomenon known as the software reliability growth model. Software reliability growth models are used to predict the number of faults and reliability of the software. In the view of this software reliability growth models are basically differentiated as the continuous and discrete models. There is a plenty of development in the continuous models but little towards the discrete models. In this paper we have presented a discrete reliability growth model with different discrete testing effort functions and the same time software release policy is discussed. A new imperfect debugging discrete software reliability growth model with testing effort is proposed. All calculations are done on real data. The results shows the proposed testing effort models are perfectly fit to the data.

—Recently, the phenomenon of software aging, one in which the state of the software system degrades with time, has been reported. This phenomenon, which may eventually lead to system performance degradation and/or crash/hang failure, is the result of exhaustion of operating system resources, data corruption, and numerical error accumulation. To counteract software aging, a technique called software rejuvenation has been proposed, which essentially involves occasionally terminating an application or a system, cleaning its internal state and/or its environment, and restarting it. Since rejuvenation incurs an overhead, an important research issue is to determine optimal times to initiate this action. In this paper, we first describe how to include faults attributed to software aging in the framework of Gray's software fault classification (deterministic and transient), and study the treatment and recovery strategies for each of the fault classes. We then construct a semi-Markov reward model based on workload and resource usage data collected from the UNIX operating system. We identify different workload states using statistical cluster analysis, estimate transition probabilities, and sojourn time distributions from the data. Corresponding to each resource, a reward function is then defined for the model based on the rate of resource depletion in each state. The model is then solved to obtain estimated times to exhaustion for each resource. The results of the semi-Markov reward model are then fed into a higher-level availability model that accounts for failure followed by reactive recovery, as well as proactive recovery. This comprehensive model is then used to derive optimal rejuvenation schedules that maximize availability or minimize downtime cost.

Software is malleable in the design phase, once the architectural design and secure modeling are set, and then after the cost and complexity of making changes rises tremendously. Security has become a high priority issue in software engineering. Design for securability is an approach to obtain distributed information systems possible to secure during software-engineering practices and processes. This can lead to the creation of secure software, as software that realizes with justifiably high confidence. The purpose of this paper is to explain the use of Software Design as its Architecture and Secure Modeling with respect to the security in the design phase of software development. The Objective is to have Security by Design or Privacy by Design. System is implemented and tested using good engineering principles and analysis techniques to avoid security problems or detect them at an early stage. To achieve this, the security should be implemented right from the inception of software and planned for each phase of SDLC in software engineering. The major security protocol analysis, sound engineering methods for security is a long way, when it comes to provide security. The cost over time for a secure design is likely to be quite different from that of less secure systems.

During the past few Decades, many software reliability growth models have been suggested for estimating reliability of software as software reliability growth models. The Functions suggested were non-linear in nature, so it was difficult to estimate the proper parameters. An Estimation method based on Ant Colony Algorithm in which parameters are estimated is discussed in this paper. In this paper, Numerical examples which have been based on five sets of real failure data have been discussed Using existing methods viable solutions for some of the models and data sets cannot be obtained, where as in the proposed method, at least one solution can be obtained. The accuracy of the results using proposed method when compared with PSO algorithm has higher accuracy for at least 10 times for majority of the models KEYWORDS Software Reliability Growth Model, Estimation, Particle Swam Optimization, Ant Colony Algorithm

Software systems have become integral part of everyday life and dependency on these makes the assessment of their reliability, a crucial task in software development. To facilitate the assessment of software reliability, effective tools and mechanisms are required. Classical approaches such as hypothesis testing are significantly time consuming as the conclusion can only be drawn after collecting huge amounts of data. Statistical methods like Sequential Analysis can be applied to arrive at a decision quickly. We propose to implement Sequential Probability Ratio Test (SPRT) for Burr Type III model based on time domain data. For this, parameters are estimated using Maximum Likelihood Estimation to apply SPRT on real time software failure datasets borrowed from different software projects