Software Reliability

Many organizations want to predict the number of defects (faults) in software systems, before they are deployed, to gauge the likely delivered quality and maintenance effort. To help in this, numerous software metrics and statistical models have been developed, with a correspondingly large literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the "quality" of the development process, or take a multivariate approach. The authors of the models have often made heroic contributions to a subject otherwise bereft of empirical studies. However, there are a number of serious theoretical and practical problems in many studies. The models are weak because of their inability to cope with the, as yet, unknown relationship between defects and failures. There are fundamental statistical and data quality problems that undermine model validity. More significantly many prediction models tend to model only part of the underlying problem and seriously misspecify it. To illustrate these points the "Goldilock's Conjecture," that there is an optimum module size, is used to show the considerable problems inherent in current defect prediction approaches. Careful and considered analysis of past and new results shows that the conjecture lacks support and that some models are misleading. We recommend holistic models for software defect prediction, using Bayesian Belief Networks, as alternative approaches to the single-issue models used at present. We also argue for research into a theory of "software decomposition" in order to test hypotheses about defect introduction and help construct a better science of software engineering.

Standards concerned with the development of safety-critical systems, and the software in such systems in particular, abound today as the software crisis increasingly affects the world of embedded computer-based systems. The use of formal methods is often advocated as a way of increasing confidence in such systems. This paper examines the industrial use of these techniques, the recommendations concerning formal methods in a number of current and draft standards, and comments on the applicability and problems of using formal methods for the development of safety-critical systems of an industrial scale. Some possible future directions are suggested.

Winner of the IEE Charles Babbage Premium award, 1994. Other versions issued as a Oxford University Computing Laboratory Technical Report PRG-TR-5-92, and Chapter 1 in Towards Verified Systems.

The methods that are usually employed to select the Enterprise Resource Planning (ERP) software package are tedious and inefficient. Selection of inappropriate software packages can lead to complexity and overrun cost which affects the organisation in the long run. This paper mainly focuses on (i) Software evaluation criteria (ii) Comparison between different methods and, (iii) Sensitivity Analysis model to assist the decision-makers to choose efficient software packages. The comparative study facilitates selection of robust software along with the best methodology employed. Four methods have been selected for comparison: Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Combination of DEMATEL and TOPSIS and Complex Proportional Assessment (COPRAS). In addition to this, a mathematical model known as Sensitivity Analysis is conducted to rate the software packages based on index value. The approach is modeled to assist the decision-makers in selecting the appropriate software packages. A numerical application at the end illustrates the proposed methodology.

Software architecture evaluation has been proposed as a means to achieve quality attributes such as maintainability and reliability in a system. The objective of the evaluation is to assess whether or not the architecture will lead to the desired quality attributes. Recently, there have been a number of evaluation methods proposed. There is, however, little consensus on the technical and non-technical issues that a method should comprehensively address and which of the existing methods is most suitable for a particular issue. This paper presents a set of commonly known but informally described features of an evaluation method and organizes them within a framework that should offer guidance on the choice of the most appropriate method for an evaluation exercise. In this paper, we use this framework to characterise eight SA evaluation methods.

Abstract—The growing diversity of web-client platform configurations causes websites to vary unpredictably, creating a myriad of challenges during software development life cycle (SDLC). This eventually affects websites user experience (UX). Exploratory heuristic evaluation (EHE) and lab-based usability testing (LBUT) are popular usability evaluation methods (UEM) that could be used to measure and improve the websites UX. Hence, the objective of this study is to derive and validate the EHE and LBUT process flow for website cross browser compatibility. In addition to finding compatibility defects using EHE, it is important to determine the reliability of these defects using LBUT. When it comes to improving websites UX, root cause analysis (RCA) is performed and recommendation are provided to the design and development team. From the results, it can be concluded that the compatibility guideline and process flow developed can improve the productivity and reliability of the EHE and LBUT methodologies.

Software Reliability is the probability of failure free software which work for a specified period of time in a specified environment. Software Reliability is also an important factor affecting system reliability. In the Existing, Software management approaches like CMM and SPICE gives the quantitative model of software process management. But these methods are based on the accuracy and the reliability of the input data to the system. This paper estimates the reliability factor issues of data for different metrics of software process model under different criteria. This paper is a survey which can be used to design a reliability system based on soft technique

Testing software in air traffic control systems costs much more than building them. Software engineers strive to find methodological and process-level solutions to balance costs and to better distribute verification efforts among all development phases. Modeldriven approaches could provide a solution. // s3pie.indd 42 3/29/13 5:40 PM m ay/J u n E 2 0 13 | I E E E S o f t wa r E 43

Abstruct-Software measurement, like measurement in any other discipline, must adhere to the science of measurement if it is to gain widespread acceptance and validity. The observation of some very simple, but fundamental, principles of measurement can have an extremely beneficial effect on the subject. Measurement theory is used to highlight both weaknesses and strengths of software metrics work, including work on metrics validation. We identify a problem with the well-known Weyuker properties, but also show that a criticism of these properties by Cherniavsky and Smith is invalid. We show that the search for general software complexity measures is doomed to failure. However, the theory does help us to define and validate measures of specific complexity attributes. Above all, we are able to view software measurement in a very wide perspective, rationalising and relating its many diverse activities.

A number of analytical models have been proposed during the past 15 years for assessing the reliability of a software system. In this paper we present an overview of the key modeling approaches, provide a critical analysis of the underlying assumptions, and assess the limitations and applicability of these models during the software development cycle. We also propose a step-by-step procedure for fitting a model and illustrate it via an analysis of failure data from a mediumsized real-time command and control software system.

System and application availability continues to be a fundamental characteristic of IT services. In recent years our IT Operations team  has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective maintenance, root cause analysis, preventative engineering, automated alerting, and workflow automation significant progress has been made in meeting availability SLAs (Service Level Agreements). This paper presents the background of this work, approach, details of its implementation, and results. A special focus is provided on the use of a classical ITIL view as operationalized in an Agile and DevOps environment.

Software quality engineering is concerned with building software products with required quality and assessing the level of quality. Software processes are important assets in achieving and assessing the software quality. Furthermore the adherence to defined processes is a key issue to having the software engineering under control and to enable process improvement. In this paper, the Software Quality Assurance (SQA) key process area in the Capability Maturity Model (CMM) is elaborated, i.e. activities for monitoring adherence to the processes. The term SQA creates some misconceptions and confusions with the general quality management concept. The SQA concept is compared to quality management and the differences and similarities are elaborated in order to eliminate the misconceptions. Furthermore, some experiences from supporting the implementation of the SQA concepts in two different software companies are briefly reported.

Fault tolerance, reliability and resilience in Cloud Computing are of paramount importance to ensure continuous operation and correct results, even in the presence of a given maximum amount of faulty components. Most existing research and implementations focus on architecture-specific solutions to introduce fault tolerance. This implies that users must tailor their applications by taking into account environment-specific fault tolerant features. Such a need results in non transparent and inflexible Cloud environments, requiring too much effort to developers and users. This paper introduces an innovative perspective on creating and managing fault tolerance that shades the implementation details of the reliability techniques from the users by means of a dedicated service layer. This allows users to specify and apply the desired level of fault tolerance without requiring any knowledge about its implementation.

This paper presents and discusses the rationale behind a method for structuring complex computing systems by the use of what we term "recovery blocks," "conversations," and "fault-tolerant interfaces." The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.

A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability.

Software reliability growth models (SRGMs) have been developed to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. Issues such as imperfect debugging and the learning phenomenon of developers have been considered in these models. However, most SRGMs assume that faults detected during tests will eventually be removed. Consideration of fault removal efficiency in the existing models is limited. In practice, fault removal efficiency is usually imperfect. This paper aims to incorporate fault removal efficiency into software reliability assessment. Fault removal efficiency is a useful metric in software development practice and it helps developers to evaluate the debugging effectiveness and estimate the additional workload. In this paper, imperfect debugging is considered in the sense that new faults can be introduced into the software during debugging and the detected faults may not be removed completely. A model is proposed to integrate fault removal efficiency, failure rate, and fault introduction rate into software reliability assessment. In addition to traditional reliability measures, the proposed model can provide some useful metrics to help the development team make better decisions. Software testing data collected from real applications are utilized to illustrate the proposed model for both the descriptive and predictive power. The expected number of residual faults and software failure rate are also presented.

This paper develops an availability model of a virtualized system. We construct non-virtualized and virtualized two hosts system models using a two-level hierarchical approach in which fault trees are used in the upper level and homogeneous continuous time Markov chains (CTMC) are used to represent submodels in lower level. In the models, we incorporate not only hardware failures (e.g., CPU, memory, power, etc) but also software failures including Virtual Machine Monitor (VMM), Virtual Machine (VM), and application failures. We also incorporate high availability (HA) service and VM live migration in the virtualized system. Metrics we use are system steady state availability, downtime in minutes per year and capacity oriented availability. 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing 978-0-7695-3849-5/09 $26.00

The designed hand gripper mechanism in this work is a two jaw actuated gripper which is different from the conventional cam and follower gripper in the way that controlled movement of the jaws is done with the help of human hand and muscular power. In this case study we have focused the attention on the dimensional of the mechanisms in two-finger grippers that we have named as gripping mechanisms to emphasize on their basic gripping purpose. The design problem has been approached for the basic characteristics of gripping mechanisms. A case of study has been reported as a digital prototype to show the soundness of the proposed optimization procedure by using finite element analysis to computational and practical results. Abstract: In order to reduce the decline of the performance of the mechanism caused by the uncertainty in the manufacturing process, the paper gives A reliability-based geometric nonlinear topology optimization design method for compliant mechanisms. The objective function satisfies the stiffness and compliance requirements of the mechanism with minimum mean flexibility and maximum geometric gain; The probability constraint of the degree of reliability is calculated by the one-time reliability method; the sensitivity analysis of the objective function uses the adjoin solution technique; the optimization uses the solid isotropic material interpolation method and iteratively solves it with the moving asymptotic method. Finally, to be compliant the reliability topology optimization of the clamp is studied as an example, and the reliability-based topology optimization is obtained.

The paper is based on Fuzzy Logic (FL) and Neural Network (NN) techniques to predict the software reliability using the MATLAB toolbox. There are four methods used in this paper to predict reliability of the dataset retrieved from John Musa of bell laboratories. These methods are fuzzy method, neural network, fuzzy-neural network and neural-fuzzy. After the assessment of data the results we achieved were best from the fuzzy-neural method among all proposed methods. In Fuzzy-neural method the Levenberg-Marquardt algorithm is used for training the neurons. The performance of our proposed approaches has been tested using the testing data, which 15% of the data from failure data set.

This paper examines the most widely used reliability modeLs. The models discussed fall into two categories, the data domain and the time domain. Besides tracing the historical development of the various models their advantages and disadvantages are analyzed. This includes models based on discrete as weil as continuous probability distributions. How well a given model performs its purpose in a specific economic environment will determine the usefulness of the model. Each of the models is examined with actual data as to the applicability of the error fmding process.

In recent years, many people have devoted their efforts to the issue of quality of Web site. The concept of quality is consisting of many criteria: quality of service perspective, a user perspective, a content perspective or indeed a usability perspective. Because of its possible instant worldwide audience a Website's quality and reliability are crucial. The very special nature of the Web applications and Websites pose unique software testing challenges. Webmasters, Web applications developers, and Website quality assurance managers need tools and methods that can match up to the new needs. This research conducts some tests to measure the quality of e government web site of five Asian countries via web diagnostic tools online. The result of this study confirmed that the Egovernment websites of Asian are neglecting performance and quality criteria. 2009 International Conference on Information Management and Engineering 978-0-7695-3595-1/09 $25.00

One of the motivations for specifying software architectures explicitly is the use of high level structural design information for improved control and prediction of software system quality attributes. In this paper, we present an approach for determining the reliability of component-based software architectures.

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.

There is great interest in ensuring correctness of safety-critical embedded systems since on the one hand the use of software gives greatly increased functionality and flexibility and on the other hand it provides unprecedented possibilities for errors. Formal methods are one technique that could improve the situation. Their use is now being suggested by an increasing number of standards in the safety-critical area. The paper compares the recommendations given by a number of important existing and emerging standards and tries to identify future trends in this area. A bibliography of standards and related publications is included

This paper examines the theoretical and practical role of testing in software development. We prove a fundamental theorem showing that properly structured tests are capable of demonstrating the absence of errors in a program. The theoremts proof hinges on our definition of test reliability and validity, but its practical utility hinges on being able to show when a test is ac-tuaUy reliable.

A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability.

termination of the manpower peak. The ratio of manpower peak to completion date is quite precise and is the best method of determining the delivery data. Cumulative manpower plots are particularly unsuited for any type of predictions, changing most slowly at the time when one desires the most information. We have shown how valuable management information can be obtained by laying out the data in a systematic manner.

Agile development methodologies have been gaining acceptance in the mainstream software development community. While there are numerous studies of Agile development in academic and educational settings, there has been little detailed reporting of the usage, penetration and success of Agile methodologies in traditional, professional software development organizations. We report on the results of an empirical study conducted at Microsoft to learn about Agile development and its perception by people in development, testing, and management. We found that one-third of the study respondents use Agile methodologies to varying degrees, and most view it favorably due to improved communication between team members, quick releases and the increased flexibility of Agile designs. The Scrum variant of Agile methodologies is by far the most popular at Microsoft. Our findings also indicate that developers are most worried about scaling Agile to larger projects (greater than twenty members), attending too many meetings and the coordinating Agile and non-Agile teams.

Software plays an important role in every field of human activity today varying from medical diagnosis to remote controlling spacecraft. Hence it is important for the software to provide failure-free performance whenever needed. The Information technology industry has witnessed rapid growth in the recent past. The competition among the firms also increased. The software organization in the developing countries like India can no longer survive on cost advantage alone. The software companies need to deliver reliable and quality software on time. A lot of research has been carried out on software quality management and reliability estimation. The objective of this paper is to provide a brief review of the major research contribution in the field of software reliability and identify the future research areas in software reliability estimation and prediction Keywords: software reliability growth models, nonhomogeneous Poisson process models, s-shaped models, imperfect debugging I. INTRODUCTION Many organizations utilize information technology (IT) to improve productivity, enhance operational efficiency, responsiveness, etc [1] As a result, the IT industry has witnessed tremendous growth in the past few decades. As the number of information technology companies increased, the competition among them also increased. The software organization in the developing countries like India can no longer survive or grow based on cost advantage alone. But delivering reliable and quality software on time within budgeted cost is a challenge for many organizations [2], [3]. Many times the companies would compromise on software testing and release the software with residual defects. This would make the software unreliable. The software reliability is defined as the probability of failure-free operation of a software system for a specified time in a specified environment [4]. The failure of the software during operations can lead to customer dissatisfaction, loss of market share, etc. The failure of a software used in the medical device or that used in air traffic control system can have a disastrous effect on the individual as well as society. Hence it is imperative for the software firms to ensure their product is sufficiently reliable before releasing the software for usage. This paper is a brief review of the important developments happened in the field of software reliability and identifies the future research areas. The remaining part of this article is arranged as follows: the session II describes the literature review methodology, the literature review analysis is given in session III and the conclusion are discussed in session IV. II. LITERATURE REVIEW METHODOLOGY A lot of articles have been presented at conferences, published in journals and books have been written in the last few decades on software reliability estimation and prediction. The aim of this paper is to provide a brief review of the important researches carried on developing software reliability models. The process started with searching for relevant published articles. The scope of the review is limited to the published books and papers published in journals and important conference proceedings. The databases searched are IEEE explore, Science direct, Google scholar and research gate. Two hundred and nine papers are identified for review. After reading the abstract, ninety-seven papers are shortlisted for review. Another twenty-nine papers are later dropped as the content is not directly related to the focus area of the review. Finally, sixty-eight papers are included in the review. The details are given in fig 1.

Traditional approaches to software reliability modeling are black box-based; that is, the software system is considered as a whole, and only its interactions with the outside world are modeled without looking into its internal structure. The black box approach is adequate to characterize the reliability of monolithic, custom, built-to-specification software applications. However, with the widespread use of object oriented systems design & development, the use of component-based software development is on the rise. Software systems are developed in a heterogeneous (multiple teams in different environments) fashion, and hence it may be inappropriate to model the overall failure process of such systems using one of the several software reliability growth models (black box approach). Predicting the reliability of a software system based on its architecture, and the failure behavior of its components, is thus essential. Most of the research efforts in predicting the reliability of a software system based on its architecture have been focused on developing analytical or state-based models. However, the development of state-based models has been mostly ad hoc with little or no effort devoted towards establishing a unifying framework which compares & contrasts these models. Also, to the best of our knowledge, no attempt has been made to offer an insight into how these models might be applied to real software applications. This paper proposes a unifying framework for state-based models for architecture-based software reliability prediction. The state-based models we consider are the ones in which application architecture is represented either as a discrete time Markov chain (DTMC), or a continuous time Markov chain (CTMC). We illustrate the DTMC-based, and CTMC-based models using examples. A detailed discussion of how the parameters of each model may be estimated, and the life cycle phases when the model may be applied is also provided.

Safety critical software requires integrating verification techniques in software development methods. Software architectures must guarantee that developed systems will meet safety requirements and safety analyses are frequently used in the assessment. Safety engineers and software architects must reach a common understanding on an optimal architecture from both perspectives. Currently both groups of engineers apply different modelling techniques and languages: safety analysis models and software modelling languages. The solutions proposed seek to integrate both domains coupling the languages of each domain. It constitutes a sound example of the use of language engineering to improve efficiency in a software-related domain. A model-driven development approach and the use of a platform-independent language are used to bridge the gap between safety analyses (failure mode effects and criticality analysis and fault tree analysis) and software development languages (e.g. unified modelling language). Language abstract syntaxes (metamodels), profiles, language mappings (model transformations) and language refinements, support the direct application of safety analysis to software architectures for the verification of safety requirements. Model consistency and the possibility of automation are found among the benefits.

A management model for explaining software errors is developed and estimated. The model is used to analyze two years of error log data at a commercial site. The focus is on identifying managerially controllable factors which affect software reliability. At the research site, application systems which (1) underwent frequent modification; (2) were maintained by programmers with low levels of application experience; (3) had high reliability requirements, and (4) had high levels of static complexity all showed particularly high error rates, other things being equal. It is suggested that that managers can make quantified judgements about the degree to which they wish to reduce error rates by implementing a number of procedures, including enforcing release control, assigning more experienced maintenance programmers, and establishing and enforcing complexity metric standards.

Software is ubiquitous in our daily life. It brings us great convenience and a big headache about software reliability as well: Software is never bug-free, and software bugs keep incurring monetary loss of even catastrophes. In the pursuit of better reliability, software engineering researchers found that huge amount of data in various forms can be collected from software systems, and these data, when properly analyzed, can help improve software reliability. Unfortunately, the huge volume of complex data renders the analysis of simple techniques incompetent; consequently, studies have been resorting to data mining for more effective analysis. In the past few years, we have witnessed many studies on mining for software reliability reported in data mining as well as software engineering forums. These studies either develop new or apply existing data mining techniques to tackle reliability problems from different angles. In order to keep data mining researchers abreast of the latest development in this growing research area, we propose this paper on data mining for software reliability. In this paper, we will present a comprehensive overview of this area, examine representative studies, and lay out challenges to data mining researchers.

Several tools have been developed for the estimation of software reliability. However, they are highly specialized in the approaches they implement and the particular phase of the software life-cycle in which they are applicable. There is an increasing need for a tool that can be used to track the quality of a software product during the software development process, right from the architectural phase all the way up to the operational phase of the software. Also the conventional techniques for software reliability evaluation, which treat the software as a monolithic entity are inadequate to assess the reliability of heterogeneous systems, which consist of a large number of globally distributed components. Architecture-based approaches are essential to predict the reliability and performance of such systems. This paper presents the high-level design of a Software Reliability Estimation and Prediction Tool (SREPT), that offers a unified framework containing techniques (including the architecture-based approach) to assist in the evaluation of software reliability at all phases of the software life-cycle.

Open source communities have successfully developed a great deal of software. Due to its free availability and highly secure operating system environment it is promoted by most of the countries all over the world. India also started contributing in this field. Government of India is also promoting usage of Open Source softwares due to its economical feasibility and security. Due to its huge demand in software field a major concern is of its reliability, which is defined as the probability of failure-free software operation for a specified period of time in a given environment.

In this paper, a cost model with warranty cost, time to remove each error detected in the software system, and risk cost due to software failure is developed. A software reliability model based on non-homogeneous Poisson process is used. The optimal release policies to minimize the expected total software cost are discussed. A software tool is also developed using Excel and Visual Basic to facilitate the task of determining the optimal software release time. Numerical examples are provided to illustrate the results.

Since 2002, over 10% of total cyber vulnerabilities were SQL injection vulnerabilities. Since most developers are not experienced software security practitioners, a solution for correctly fixing SQL injection vulnerabilities that does not require security expertise is desirable. In this paper, we propose an automated method for removing SQL injection vulnerabilities from Java code by converting plain text SQL statements into prepared statements. Prepared statements restrict the way that input can affect the execution of the statement. An automated solution allows developers to remove SQL injection vulnerabilities by replacing vulnerable code with generated secure code. In a formative case study, we tested our automated fix generation algorithm on five toy Java programs which contained seeded SQL injection vulnerabilities and a set of object traceability issues. The results of our case study show that our technique was able remove SQL injection vulnerabilities in five different statement configurations.

Most Software Reliability Growth Models (SRGMs) ignore either the learning phenomenon of the testing team or quality of debugging or both for the sake of simplicity. Some SRGMs consider imperfect debugging tightly integrated with the learning phenomenon. In practice, imperfect debugging and the learning phenomenon of the testing team are independent of each other since the former indicates quality of debugging process and the latter the quality of the testing process, usually carried out by different teams. Furthermore, sometimes the debugging team may find and correct some more faults in addition to the faults which caused the failures leading to what is known as efficient debugging. In this paper, a new mathematically derived SRGM is proposed, which also provides estimates of various quality metrics such as the number of faults in the software system, debugging and the learning indices. The proposed SRGM seems to describe the time to failure data of many software projects satisfactorily.

Software industry suffer many challenges in developing a high quality reliable software. Many factors affect their development such as the schedule, limited resources, uncertainty in the developing environment and inaccurate requirement specification. Software Reliability Growth Models (SRGM) were significantly used to help in solving these problems by accurately predicting the number of faults in the software during both development and testing processes. The issue of building growth models was the subject of many research work. In this paper, we explore the use of fuzzy logic to build a SRGM. The proposed fuzzy model consists of a collection of linear sub-models joined together smoothly using fuzzy membership functions to represent the fuzzy model. Results and analysis based data set developed by John Musa of Bell Telephone Laboratories [1] are provided to show the potential advantages of using fuzzy logic in solving this problem.

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.

Grid scheduling is one of the prime challenges in grid computing. Reliability, efficiency (with regard to time utilization), effectiveness in resource usage, as well as robustness tend to be the demanded features of Grid scheduling systems. A number of algorithms have been designed and developed in order to make effective Grid scheduling. Project management is the well known area of operation research. Here we are proposing a new prioritized deadline based scheduling algorithm (PDSA) using project management technique for efficient job execution with deadline constraints of users' jobs. An extensive performance comparison has been presented using synthetic workload traces to evaluate the efficiency and robustness of grid scheduling with respect to average turnaround times and maximum tardiness. Result has shown improved performance under dynamic scheduling environment. Based on the comparative performance analysis, proposed PDSA has shown the optimal performance as compared to EDF and RR scheduling algorithms under dynamic scheduling environment. In brief, PDSA is the true application of project management in grid computing.

As smart phones grow in popularity, manufacturers are in a race to pack an increasingly rich set of features into these tiny devices. This brings additional complexity in the system software that has to fit within the constraints of the devices (chiefly memory, stable storage, and power consumption) and hence, new bugs are revealed. How this evolution of smartphones impacts their reliability is a question that has been largely unexplored till now. With the release of open source OSes for hand-held devices, such as, Android (open sourced in October 2008) and Symbian (open sourced in February 2010), we are now in a position to explore the above question. In this paper, we analyze the reported cases of failures of Android and Symbian based on bug reports posted by thirdparty developers and end users and documentation of bug fixes from Android developers. First, based on 628 developer reports, our study looks into the manifestation of failures in different modules of Android and their characteristics, such as, their transience in time. Next, we analyze similar properties of Symbian bugs based on 153 failure reports. Our study indicates that Development tools, Web browsers, and Multimedia applications are most error-prone in both these systems. We further analyze 233 bug fixes for Android and categorized the different types of code modifications required for the fixes. The analysis shows that 78% of errors required minor code changes, with the largest share of these coming from modifications to attribute values and conditions. Our final analysis focuses on the relation between customizability, code complexity, and reliability in Android and Symbian. We find that despite high cyclomatic complexity, the bug densities in Android and Symbian are surprisingly low. However, the support for customizability does impact the reliability of mobile OSes and there are cautionary tales for their further development.

We present a method for optimizing software testing efficiency by identifying the most error prone path clusters in a program. We do this by developing variable length genetic algorithms that optimize and select the software path clusters which are weighted with sources of error indexes. Although various methods have been applied to detecting and reducing errors in a whole system, there is little research into partitioning a system into smaller error prone domains for testing. Exhaustive software testing is rarely possible because it becomes intractable for even medium sized software. Typically only parts of a program can be tested, but these parts are not necessarily the most error prone. Therefore, we are developing a more selective approach to testing by focusing on those parts that are most likely to contain faults, so that the most error prone paths can be tested first. By identifying the most error prone paths, the testing efficiency can be increased.

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

Owners of industries traveling around the world need some system from where they can monitor and keep control of their industry remotely. Speedily advancing software technologies have made it possible to develop a new generation of Supervisory Control and Data-Acquisition (SCADA) system. This generation of SCADA is known as PROPOSED SCADA SYSTEMS. This paper present a concept of how industrial and commercial areas can be monitored and controlled via SCADA system from anywhere in the world on supported portable devices. Through Remote access of SCADA, the Remote Desktop Protocol (ROP) brings SCADA real-time and passes information to users. The presented solution provides reduced software costs while improving reliability. The system also allows Open Process Control (OPC) linkage between the controller and SCADA systems. An additional monitoring approach in this system is lP CAMERAS based online visual monitoring. Life videos of the industry and commercial area can be viewed from anywhere in the globe on the portable devices. The proposed concept is simulated on two prototype industrial plants 1) An automated Car Wash System and 2) Load Cell based luggage segregation. Proposed system was tested on simulator and show remarkable results.

In many cases, it is possible to derive a quantitative reliability or availability assessment for systems containing software with the appropriate use of system-level measurement-based modeling and supporting data. This paper demonstrates the system-...