Fault Removal Efficiency Research Articles

NHPP based Testing Coverage model with Fault Removal Efficiency and Error Generation

NHPP based Testing Coverage model with Fault Removal Efficiency and Error Generation

An SRGM Using Fault Removal Efficiency and Correction Lag Function

Software reliability growth models (SRGMs) have shown remarkable research since the early 1970s. Usually, growth models are used throughout the software testing process to determine the failure pattern, consumption of testing efforts, total expected cost and reliability. Many existing models start with the underlying premise that defects are found and immediately fixed. But in reality, initially faults are detected, isolated and then corrected. This study enhances the SRGM by including the detection and correction procedure to address this discrepancy, considering the correction lag function and fault removal efficiency. In addition, this study also examines the overall anticipated cost and reliability associated with the suggested model. Furthermore, numerical outcomes are shown using actual datasets to verify the validity of the proposed model.

Empowering software reliability: Leveraging efficient fault detection and removal efficiency

Advancements in science and technology have led to the widespread use of computer systems in various applications, emphasizing the importance of software reliability. Software failures can have severe consequences, making thorough testing crucial. Software reliability growth models (SRGMs) play a significant role in enhancing reliability by predicting improvement over time. This article introduces a comprehensive approach to software reliability that incorporates a dynamic fault detection rate, along with fault removal efficiency. The fault detection rate measures the rate at which faults are identified during testing, reflecting the effectiveness of the testing process. By incorporating this dynamic component, the model provides a more accurate estimation of software reliability and enables adaptive testing strategies and resource allocation. Achieving a high fault detection rate is desirable, but organizations must consider the cost implications and strike a balance between reliability and time-to-market constraints. This article extends the analysis to calculate the optimal release time and optimal warranty period that minimize development costs, subject to the desired reliability. By considering these factors, development teams can make informed decisions regarding the timing of software release and the duration of the warranty period, optimizing both reliability and cost.

A software reliability model incorporating fault removal efficiency and it’s release policy

A software reliability model incorporating fault removal efficiency and it’s release policy

Testing coverage‐based software reliability growth model considering uncertainty of operating environment

AbstractSoftware reliability is one of the standard critical inherent characteristics of software systems. The testing coverage function (TCF) is a significant parameter for identifying the completeness and effectiveness of software testing. It is defined as the proportion of the code that has been tested up to timet. To capture the dynamic behavior of the number of faults detected over a period of time, several distributions, namely S‐shaped, inflection S‐shaped, logistic, log‐logistic, Weibull, Rayleigh, Erlang, and logarithmic exponentiated, have been used as TCF in literature. However, these distributions are not sufficient to describe TCF's practical behavior due to complexity and vagueness in the collected data. This study proposes two software reliability growth models (SRGMs), which incorporate the generalized inflection S‐shaped (GISS) distribution as TCF. The models have been developed in perfect and imperfect debugging environments while considering fault removal efficiency, error generation, and uncertainty in the operating environment. To analyze the effectiveness, the proposed models are then tested with six failure data sets. The choice of GISS distribution as a TCF improves the software reliability estimation in comparison with the existing models in the literature. Finally, single and multiple parameters sensitivity analysis also has been done and based on it, the critical parameters have been detected. The proposed models may be helpful for the system analyst to predict various parameters about some software systems.

A Software Reliability Model Using Fault Removal Efficiency

With the increase of human dependency over computer software, considerable effort has been given to determine software reliability effectively. A huge variety of software reliability growth models (SRGMs) have been developed to explain statistically how system reliability varies over time by monitoring the failure data sets during the testing process. The paper proposes a new SRGM based on taking into account the fault removal efficiency which is the ratio of corrected and detected faults during the testing process. The new model is compared to some known model from the relevant literature for two certain data sets and it turns out to perform better in terms of four GOF benchmarks.

Unified framework to assess software reliability and determine optimal release time in presence of fault reduction factor, error generation and fault removal efficiency

Unified framework to assess software reliability and determine optimal release time in presence of fault reduction factor, error generation and fault removal efficiency

Software Reliability Growth Modeling Involving Burr Type XII distribution and Fault Removal Efficiency

Software Reliability Growth Modeling Involving Burr Type XII distribution and Fault Removal Efficiency

NHPP-Based Software Reliability Growth Modeling and Optimal Release Policy for N-Version Programming System with Increasing Fault Detection Rate under Imperfect Debugging

Attempts have been made to propose a software reliability growth model (SRGM) based on non-homogeneous Poisson process for NVP system. The proposed model concerns the combined effect of increasing fault detection rate and fault removal efficiency under imperfect debugging. Moreover, a cost model has been developed. Also, an optimal release policy based on the proposed cost model and the number of remaining faults has been discussed. The performance of the proposed model has been compared with existing SRGMs for NVP system. Toward the end, the proposed model has been validated using some real data.

Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio, fault removal efficiency, and change point

Software reliability growth models have been proposed to assess and predict the reliability growth of software, remaining number of faults, and failure rate. In previous studies, software faults have been mainly categorized into two categories based on its severity in removal process: simple faults and hard faults. In reality, fault detectability is one of the crucial factors which can influence the reliability growth of software. The detectability of a software fault depends on how frequently the instructions containing faults are executed. However, fault removability of a software fault depends on fault removal efficiency of debugging team. The main motive of this article is to incorporate the fault detectability in software reliability assessment. Fault exposure ratio is an essential factor for software reliability modeling that controls the per-fault hazard rate. It is strongly dependent on fault detectability. In this article, the effect of fault detectability, fault removability, fault exposure ratio, and fault removal efficiency has been considered simultaneously in software reliability growth modeling. Moreover, a logistic fault exposure ratio has been introduced. The effect of change point is incorporated in the proposed software reliability growth model. Two illustrative examples with software testing data have been presented.

A testing-coverage software reliability model considering fault removal efficiency and error generation.

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

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.

NHPP Software Reliability Growth Model Considering Imperfect Debugging

PDF HTML阅读 XML下载 导出引用 引用提醒 考虑不完美排错情况的NHPP 类软件可靠性增长模型 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the Science Foundation of Aeronautics of China under Grant No.20095558008 (航空科学基金) NHPP Software Reliability Growth Model Considering Imperfect Debugging Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:针对现有NHPP 类软件可靠性增长模型对故障排错过程中不完美排错情况考虑不完全的现状,提出了一 种新的软件可靠性增长模型.该模型全面考虑了不完美排错的两种情况:既考虑了排错过程中引入新错误的可能性, 又考虑了不完全排错的情况,并且引入了一种故障排除率随时间变化的故障排除率函数,使模型更符合实际情况.利 用公开发表的两组不同的软件失效数据对该模型进行验证的结果表明,与现有的对不完美排错情况考虑不完全的 模型相比,该模型能够取得更好的拟合结果和预测效果. Abstract:Addressing at the problem that the considerations of imperfect debugging phenomenon in the existing software reliability growth model are limited, this paper proposes a software reliability growth model which takes into account of the imperfect debugging comprehensively. Both the fault introduction and the fault removal efficiency are considered in this model, and a time-dependent fault removal efficiency function is introduced. The goodness-of-fit and the predictive power of the new model are examined by using a public software failure data set. The results show that compared with other existing models, the proposed model fits the failure data better and can predict this set of data more accurately. 参考文献 相似文献 引证文献

Reliability growth modeling and optimal release policy under fuzzy environment of an N-version programming system incorporating the effect of fault removal efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme), fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme). These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Considering fault removal efficiency in software reliability assessment

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.