High Reliable Software Research Articles

Software Reliability Growth Model for N-Version Fault Tolerant Software with Common and Independent Faults

Research and development teams have become increasingly focused on developing highly reliable software for safety-critical systems. It is a major challenge for real-time control systems to achieve high reliability software to meet safety standards. A reliability evaluation focuses primarily on analytical and modeling techniques for fault prediction. In safety-critical systems like nuclear plant controls, aircraft controls and railroad signalization systems, N-version programming (NVP) is an effective technique for raising software’s reliability, particularly in areas with high-risk ratios because small errors can result in hazardous incidents. It allows the software to be fault-tolerant, aiding it to produce accurate results even when the software has faults. We present an analytical method for assessing the reliability of N-version software systems. Analysis of the system’s reliability and other performance metrics is provided with closed-form expressions. As an additional extension, we conduct numerical analyses of two cases, the 2VP system and 3VP system, in which suitable parameters are used. We conduct numerical simulations using MATLAB to generate the analytical results and compare the analytical results by using numerical results and neuro-fuzzy results using fuzzy interference systems.

Applying Scrum Development on Safety Critical Systems

Scaled agile approaches are increasingly being used by automotive businesses to cope with the complexity of their organizations and products. The development of automotive systems necessitates the use of safe procedures. SafeScrum® is a real example of how agile approaches may be used in the creation of high-reliability systems on a small scale. A framework like SAFe or LeSS does not facilitate the creation of safety-critical systems in large-scale contexts from the start. User stories are a wonderful approach to convey flexible demands, the lifecycle is iterative, and testing is the initial stage in the development process. Scrum plus extra XP approaches may be used to build high-reliability software and certification by the IEC 61508 standard is required for the software. This adds a slew of new needs to the workflow. Scrum's quality assurance measures proved to be inadequate in a recent industry situation. Our study's overarching goal is to provide light on the Scrum development process so that it may be improved for use with life-or-death systems. Our study of the business world was a mixed-methods affair. The findings demonstrated that although Scrum is helpful in ensuring the security of each release, it is less nimble in other respects. The difficulties of prioritization, communication, time constraints, and preparing for and accepting new safety standards were all discussed. In addition, we have had some helpful feedback from the business world, but the generality issue arising from this particular setting has yet to be addressed.

Software Reliability Prediction through Encoder-Decoder Recurrent Neural Networks

With the growing demand for high reliability and safety software, software reliability prediction has attracted more and more attention to identifying potential faults in software. Software reliability growth models (SRGMs) are the most commonly used prediction models in practical software reliability engineering. However, their unrealistic assumptions and environment-dependent applicability restrict their development. Recurrent neural networks (RNNs), such as the long short-term memory (LSTM), provide an end-to-end learning method, have shown a remarkable ability in time-series forecasting and can be used to solve the above problem for software reliability prediction. In this paper, we present an attention-based encoder-decoder RNN called EDRNN to predict the number of failures in the software. More specifically, the encoder-decoder RNN estimates the cumulative faults with the fault detection time as input. The attention mechanism improves the prediction accuracy in the encoder-decoder architecture. Experimental results demonstrate that our proposed model outperforms other traditional SRGMs and neural network-based models in terms of accuracy.

An empirical characterization of bad practices in continuous integration

Continuous Integration (CI) has been claimed to introduce several benefits in software development, including high software quality and reliability. However, recent work pointed out challenges, barriers and bad practices characterizing its adoption. This paper empirically investigates what are the bad practices experienced by developers applying CI. The investigation has been conducted by leveraging semi-structured interviews of 13 experts and mining more than 2,300 Stack Overflow posts. As a result, we compiled a catalog of 79 CI bad smells belonging to 7 categories related to different dimensions of a CI pipeline management and process. We have also investigated the perceived importance of the identified bad smells through a survey involving 26 professional developers, and discussed how the results of our study relate to existing knowledge about CI bad practices. Whilst some results, such as the poor usage of branches, confirm existing literature, the study also highlights uncovered bad practices, e.g., related to static analysis tools or the abuse of shell scripts, and contradict knowledge from existing literature, e.g., about avoiding nightly builds. We discuss the implications of our catalog of CI bad smells for (i) practitioners, e.g., favor specific, portable tools over hacking, and do not ignore nor hide build failures, (ii) educators, e.g., teach CI culture, not just technology, and teach CI by providing examples of what not to do, and (iii) researchers, e.g., developing support for failure analysis, as well as automated CI bad smell detectors.

Testing effort based software reliability assessment incorporating FRF and change point

In today's World, to meet the demand of high quality and reliable software systems, it is imperative to perform comprehensive testing and debugging of the software code. The fault detection and removal rate may change over time. The time point after which the rates are changed is termed as the change point. Practically, the failure count may not coincide with the total fault count removed from the system. Their ratio is measured by Fault Reduction Factor (FRF). Here, we propose a Weibull testing effort dependent Software Reliability Growth Model with logistic FRF and change point for assessing the failure phenomenon of a software system. The models have been validated on two real software fault datasets. The parameters are estimated using Least squares and various criteria are employed to check the goodness of fit. The comparison is also facilitated with the existing models in literature to demonstrate that proposed model has better performance.

Improved EM algorithm in software reliability growth models

The important issue in software reliability management includes evaluation, measurement and prediction about errors in the model. In critical applications the requirement of software reliability is considered as important issues as the low reliability decreases the application performance. Several factors that affects the reliability such as consistency, test coverage, complexity, operational realism, etc. In this complexity leads to faults in the model as it is also related to reliability so that once we start to develop and design the application complexity must take into an account. In delivering some high reliability software products these above factors must be included so that the expectation about product to the user community increases. This research work describes about an efficient model for identifying the faults in the software products using improved EM algorithm using Gaussian function. The improved EM function includes k-means algorithm together to perform the task.

Comparison of Agile Method and Scrum Method with Software Quality Affecting Factors

The software industry used software development lifecycle (SDLC) to design, develop, produce high quality, reliable and cost-effective software products. To develop an application, project team used some methodology which may include artifacts and pre-defining specific deliverables. There are different SDLC process models such as waterfall, iterative, spiral and agile model available to develop a quality product. In this paper we focus only on agile software development model, and Scrum model and their techniques. There are many papers and books written on agile methodologies. We will also use their knowledge in this paper. To collect data for comparison of agile method with software quality affecting factors, an online questionnaire survey was conducted. The survey sample consisted of software developers with several years of industry experience using agile methodologies. The main purpose of this study is to compare soft-ware quality affecting factors with agile and scrum model.

A Software Reliability Model Considering the Syntax Error in Uncertainty Environment, Optimal Release Time, and Sensitivity Analysis

The goal set by software developers is to develop high quality and reliable software products. During the past decades, software has become complex, and thus, it is difficult to develop stable software products. Software failures often cause serious social or economic losses, and therefore, software reliability is considered important. Software reliability growth models (SRGMs) have been used to estimate software reliability. In this work, we introduce a new software reliability model and compare it with several non-homogeneous Poisson process (NHPP) models. In addition, we compare the goodness of fit for existing SRGMs using actual data sets based on eight criteria. The results allow us to determine which model is optimal.

Making Multivariant Programming Practical and Inexpensive

Since it was first proposed in 1978, n-version programming (NVP) has been a mainstay technique for obtaining fault tolerance in high-reliability software. Although subsequent research on NVP has indicated that coincident failures across different versions can be more common than initially expected, NVP remains the “gold standard” for application domains in which high reliability is of critical importance, such as fly-by-wire avionics control software.

Simultaneous Localization of Software Faults Based on Complex Network Theory

Effective debugging is necessary for producing high quality and reliable software. Fault localization plays a vital role in the debugging process. However, fault localization is the most tedious and expensive activity in program debugging, as such, effective fault localization techniques that can identify the exact location of faults is eminent. Despite various fault localization techniques proposed, their application in multiple-fault programs is limited. The presence of multiple faults in a program reduces the efficacy of the existing fault localization techniques to locate faults effectively due to fault interference. Moreover, most of these techniques are unable to localize faults simultaneously. This has led researchers to adopt alternative approaches, such as one-fault-at-a-time debugging and parallel debugging. In this paper, we propose a novel fault localization technique based on complex network theory (FLCN) to improve localization effectiveness in programs with single-fault and multiple-fault and to aid developers to localize multiple faults simultaneously in a single diagnosis rank list. The proposed technique ranks faulty statements based on their behavioral abnormalities and distance between faulty statements in both passed and failed test executions. Two graph-based centrality measures are adopted for fault diagnosis, namely, degree centrality and closeness centrality and a new ranking formula is proposed. FLCN is evaluated across 14 subjects with both single-fault and multiple-fault programs. Our experimental results show that FLCN is more effective at locating faults when compared with existing state-of-the-art fault localization techniques with improvement of fault localization effectiveness in both single-fault and multiple-fault programs.

REQUIREMENT UNDERSTANDABILITY QUANTIFICATION MODEL OF OBJECT ORIENTED SOFTWARE

Understandability has significant role in development of quality software; it incredibly impacts cost, quality and unwavering quality at the time software development (especially at early stages of development). Wrong interpretation prompts ambiguities, misconception and thus the misinterpretations of further development process and the related records, which frequently results to defective development. Notwithstanding the way that understandability is essential and very critical viewpoint for software development process. Understandability is considered as basic building block for delivering high quality and reliable software. It greatly influences cost, quality and reliability at the time of software evolution. In this paper, author highlights the importance of understandability early at requirement phase in general and as a factor of software testability. The paper quickly portrays the proposed model for understandability quantification of object oriented software [RUMOOS] by establishing multiple linear regressions. Finally the proposed model has been validated using experimental tryout.

Development of methods for equivalent transformation of GERT networks for application in multi-version software

The usage of information and control systems in various fields of production and research requires a certain level of reliability in the functioning of these systems. One of the most promising and already positively proven methodologies to ensure high reliability and fault tolerance of software is multi-version designing. The greatest value this factor have in the areas where control system failure can result in significant financial and material losses, as well as harm to human health and life.

Optimization Problems for a Modular Software with Faults of Different Severity

For the past several decades, reliability has been presumed as the most important characteristic of any complex software system. Software developer’s assurance of high quality and reliable software is driven on the basis of efficient reliability assessment. Since 1970’s an unprecedented growth has been observed in the area of software reliability growth modeling. Software reliability growth models (SRGM) are linked to the testing stage of software development and provides for insights into ways to improve reliability of the system and optimal time to release the software. Several SRGMs have been proposed in the literature to model fault identification/removal phenomenon based on time; search for more efficient and accurate models that can fit greater number of reliability growth curves is endless. Categorization of faults lying in the software has been widely studied as well. Also, efforts have been made in the past to understand reliability issues concerned with modular software system. In this paper we examine three different types of faults lying in complex software and study their behavior in an N-module software system. In order to attain two fold objectives of maximizing reliability of such a system and minimizing overall debugging cost, we propose two related optimization models. The inbuilt model flexibility takes care of different environment. The model is validated by real life software failure data set to show its goodness of fit and applicability.

An enhanced software defect prediction model with multiple metrics and learners

Defect prediction is a critical technique for achieving high reliability software. Defect prediction models based on software metrics are able to predict which modules are fault-prone, which in turn. The prediction results would make the software developers to pay more attentions to these high-risk modules. For software defect prediction modelling, machine learning techniques have been widely employed. Model selection problem is always a challenge for generating an efficient predictor with a satisfied performance which is also always difficult to achieve. In this paper, a software defect prediction modelling framework based on multi-metric space and multi-type learning models is proposed. Different types of component classifiers and different software metric sets are used to build a software defect prediction ensemble model with the increment on the diversity of ensemble learning as far as possible. The proposed model is fully investigated by using a set of real project data from NASA MDP, the experimental results reveal that the model effectively improve the generalisation performance and the predictive accuracy.

Determining the optimal allocation of testing resource for modular software system using dynamic programming

Reliability is a major concern in the process of software development because unreliable software can cause failure in the computer system that can be hazardous. A way to enhance the reliability of software is to detect and remove the faults during the testing phase, which begins with module testing wherein modules are tested independently to remove a substantial number of faults within a limited resource. Therefore, the available resource must be allocated among the modules in such a way that the number of faults is removed as much as possible from each of the modules to achieve higher software reliability. In this article, we discuss the problem of optimal resource allocation of the testing resource for a modular software system, which maximizes the number of faults removed subject to the conditions that the amount of testing-effort is fixed, a certain percentage of faults is to be removed and a desired level of reliability is to be achieved. The problem is formulated as a non linear programming problem (NLPP), which is modeled by the inflection S-shaped software reliability growth models (SRGM) based on a non homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing-effort functions. A solution procedure is then developed using a dynamic programming technique to solve the NLPP. Furthermore, three special cases of optimum resource allocations are also discussed. Finally, numerical examples using three sets of software failure data are presented to illustrate the procedure developed and to validate the performance of the strategies proposed in this article. Experimental results indicate that the proposed strategies may be helpful to software project managers for making the best decisions in allocating the testing resource. In addition, the results are compared with those of Kapur et al. (2004), Huang and Lyu (2005), and Jha et al. (2010) that are available in the literature to deal the similar problems addressed in this article. It reveals that the proposed dynamic programming method for the testing-resource allocation problem yields a gain in efficiency over other methods.

A REVIEW ON APPROACHES AND MODELS PROPOSED FOR SOFTWARE RELIABILITY TESTING

Reliability testing of software is the key area of concern now-a-days; specially with the software which are safety critical and security prone systems. There is always a need of high software reliability. Most of the present models for evaluation of software reliability are based on statistical and probability approach. When we look into the use of these models for software reliability testing, we observe that there are possibilities of imprecision in the reliability estimation. For removing this imprecision, the Fuzzy logic and Fuzzy expert systems are used within various researches. The use of Fuzzy logic for reliability estimation can enhance the reliability of software even during the early stages of software development. Here we are giving a comparison analysis of few approaches and models proposed so far, for the estimation of software reliability and its improvement using Fuzzy logic.

OPTIMAL RELEASE TIME DECISION FROM FUZZY MATHEMATICAL PROGRAMMING PERSPECTIVE

Demand for high software reliability requires rigorous testing followed by requirement of robust modeling techniques for software quality prediction. On one side, firms have to steadily manage the reliability by testing it vigorously, the optimal release time determination is their biggest concern. In past many models have been developed and much research has been devoted towards assessment of release time of software. However, majority of the work deals in crisp study. This paper addresses the problem of release time prediction using fuzzy Logic. Here we have formulated a Fuzzy release time problem considering the cost of testing under the impact of warranty period. Results show that fuzzy model has good adaptability.

Using Statistical Usage Testing in Conjunction with Other Black Box Testing Techniques

Cleanroom methodology is a scrupulous incremental software development approach16 for the development of zero defect and high-reliability software using box structure specification, statistical quality control and certification.2 Statistical Usage Testing (SUT) technique is the testing technique defined it Cleanroom software engineering. It based on developing the usage models and then performing statistical tests on the usage models.15 The paper shows the usage of SUT in conjunction with other black box testing techniques. Other types of testing can be performed along with SUT depending on the requirement. Use of other testing techniques can be essential to show specific scenarios of use or to attain full usage model coverage with reduced test cases.17 The paper also presents the effectiveness of applying SUT in conjunction with other black box testing techniques by using various test cases. Other black box testing techniques used include Equivalence Class Partitioning (ECP), Boundary Value Analysis (BVA), Cause Effect Graphing (CEG), Use Case Testing (UCT) and Orthogonal Array Testing (OATS).

Flexibility: A Key Factor to Testability

Testability is an important software quality factor that is ineffective if it is not available at an early stage in the development life-cycle. It becomes more essential in the case of object oriented design. Flexibility is an important key factor to testability analysis and measurement for delivering high class testable and maintainable software. Flexibility is a criterion of crucial significance to software developers, designers and the quality controllers. It constantly guides and supports to avoid wastage of resources as well as enable the designers for continuous improvement in the development process. Flexibility is concerned with building high quality and reliable software within the constraints of cost and time. It greatly influences cost, quality and reliability at software evolution process. Despite the fact flexibility is vital and highly significant aspect for software development processes, it is poorly managed. This paper focuses the need and importance of flexibility early at design phase. A model has been proposed for flexibility measurement of object oriented design by establishing multiple linear regression. Finally the proposed model has been validated using experimental tryout.

A Novel Feature Subset Selection Algorithm for Software Defect Prediction

Software Defect Prediction has been an area of growing importance. It is always required to maintain high reliability and high quality for any software being developed. A software quality prediction model is built using software metrics and defect data collected from a previously developed system release or similar software project. Upon validation of such a model, it could be used for predicting the fault-proneness of program modules that are currently under development. A low quality or fault prone prediction can justify the application of available quality improvement resources to those programs. In contrast, a non fault prone prediction can justify non- application of the limited resources to these already high- quality programs. And finally high software reliability and quality are maintained with an effective use of the available resources. A feature extraction algorithm based on graph clustering is applied over the historical software data collected for defect prediction purpose and its impact on different data sets are analyzed in this paper.