Performance analysis of software reliability growth models with testing-effort and change-point

In order to overcome the limitations of debugging process, insufficient consideration of imperfect debugging and testing-effort (TE) in software reliability modeling and analysis, a software reliability growth model (SRGM) explicitly incorporating imperfect debugging and TE is developed. From the point of view of incomplete debugging and introduction of new fault, software testing process is described and a relatively unified SRGM framework is presented considering TE. The proposed framework models are fairly general models that cover a variety of the previous works on SRGM with ID and TE. Furthermore, a special SRGM incorporating an improved Logistic testing-effort function (TEF) into imperfect debugging modeling is proposed. The effectiveness and reasonableness of the proposed model are verified by published failure data set. The proposed model closer to real software testing has better descriptive and predictive power than other models

The safety-critical software of Reactor Protection System (RPS) plays a significant role for the safe operation of the nuclear power plant (NPP). However, it also brings challenges both to the reliability analysis of the RPS and to the Probabilistic Risk Assessment of the NPPs. The reliability analysis of safety-critical software is also expected by the nuclear regulation agencies and the software development groups for test evaluation and optimization. The detected faults during the software test process are regarded to have close connection with the software reliability and there have been hundreds of test-based software reliability models. Due to the particularity of its function, the safety-critical software of an NPP is especially sensitive to the faults with higher severity levels which should be paid special attention to. Severity levels of faults are commonly taken into account during software reliability modelling. In this paper, a novel software reliability growth model considering actual severity data was built based on a non-homogeneous Poisson process. Ratio of fatal faults with highest severity level to all accumulated faults was modelled with logistic curve. Then the mean value functions of both fatal and general faults were derived. A belief factor was employed to describe the trend of severity classification. In the end, the test data of a practical project was used to verify this model. The analysis results showed that this model had better prediction effect than Goel-Okumoto model and Inflection S-shaped model even when limited test data were collected. This novel model can be used as a helpful tool to evaluate both the reliability and the release time of the safety-critical software of an NPP.

In the software testing process, many factors can affected the failure distribution, such as the testing strategy, running environment and resource allocation, etc. Once these factors are transformed during testing phase, they could result in the failure intensity function increasing or decreasing non-monotonically and abrupt fluctuations at a certain time interval. Especially, in large software systems, detected complex faults and failures are more difficult to isolate, reproduce and remove in comparison of detected simple faults and failures. Therefore, the fault detection rate shows the irregular fluctuation over time in the software testing process. In this paper, we propose an imperfect software debugging model derived from an Ito -type stochastic differential equation (SDE). In the actual testing process, the cumulative number of fault detection can be considered a stochastic event. Thus, we can model fault detection process using SDE. We estimate the parameters of the proposed model using maximum likelihood estimation. We also use five historical fault data sets to test the performance of the proposed model. To show that the proposed model is the best model among all models, we use a new optimal method to select the best model from various software reliability models. Experimental results show that the proposed model has better fitting and predictive performance than non-homogeneous Poisson process models and other perfect software reliability models based on SDE.

This paper proposes a new scheme for constructing software reliability growth models (SRGM) based on a nonhomogeneous Poisson process (NHPP). The main focus is to provide an efficient parametric decomposition method for software reliability modeling, which considers both testing efforts and fault detection rates (FDR). In general, the software fault detection/removal mechanisms depend on previously detected/removed faults and on how testing efforts are used. From practical field studies, it is likely that we can estimate the testing efforts consumption pattern and predict the trends of FDR. A set of time-variable, testing-effort-based FDR models were developed that have the inherent flexibility of capturing a wide range of possible fault detection trends: increasing, decreasing, and constant. This scheme has a flexible structure and can model a wide spectrum of software development environments, considering various testing efforts. The paper describes the FDR, which can be obtained from historical records of previous releases or other similar software projects, and incorporates the related testing activities into this new modeling approach. The applicability of our model and the related parametric decomposition methods are demonstrated through several real data sets from various software projects. The evaluation results show that the proposed framework to incorporate testing efforts and FDR for SRGM has a fairly accurate prediction capability and it depicts the real-life situation more faithfully. This technique can be applied to wide range of software systems.

Many software reliability growth models have been proposed in the past decade. Those models tacitly assume that testing-effort expenditures are constant throughout software testing. This paper develops realistic software reliability growth models incorporating the effect of testing-effort. The software error detection phenomenon in software testing is modeled by a nonhomogeneous Poisson process. The software reliability assessment measures and the estimation methods of parameters are investigated. Testing-effort expenditures are described by exponential and Rayleigh curves. Least-squares estimators and maximum likelihood estimators are used for the reliability growth parameters. The software reliability data analyses use actual data. The software reliability growth models with testing-effort can consider the relationship between the software reliability growth and the effect of testing-effort. Thus, the proposed models will enable us to evaluate software reliability more realistically.

In reality, the fault detection (or correction) phenomenon and software reliability estimation in the operational phase are different from that in the testing phase. The fault removal continues at a slower rate during the operational phase. In this paper, we will investigate some techniques for software reliability prediction and measurement in the operational phase. We first review how some software reliability growth models (SRGMs) based on non-homogeneous Poisson processes (NHPPs) can be readily derived based on a unified theory for NHPP models. Under this general framework, we can not only verify some conventional SRGMs but also derive some new SRGMs that can be used for software reliability measurement in the operational phase. That is, based on the unified theory, we can incorporate the concept of multiple changepoints into software reliability modeling. We can formularize and simulate the fault detection process in the operational phase. Some numerical illustrations based on real software failure data are also presented. The experimental results show that the proposed models can easily reflect the possible changes of fault detection process and offer quantitative analysis on software failure behavior in field operation.

Software reliability testing refers to various software testing activities that are driven to achieve a quantitative reliability goal given a priori or lead to a quantitative reliability assessment for the software under test. In this paper we develop a modeling framework for the software reliability testing process, comprising a simplifying model and a generalized model. In both models the software testing action selection process and the defect removal mechanism are explicitly described. Both the discrete-time domain and the continuous-time domain are involved. The generalized model is more accurate or realistic than the simplifying model since the former avoids the assumption that defects are equally detectable and the assumption that defects are removed upon being detected. However simulation examples show that the simplifying model really captures some of essential features of the software testing process after a short initial testing stage. The modeling framework is practically realistic, mathematically rigorous, and quantitatively precise. It demonstrates that the relationship between software testing and delivered software reliability, which was poor understood, can well be formulated and quantified. Rigorous examinations show that several common assumptions adopted in software reliability modeling, including the independence assumption, the exponentiality assumption, and the NHPP assumption, are theoretically false in general. This paper sets a good starting point to further formalize and quantify the software testing process and its relation to delivered software reliability.

Software testing is an essential activity in all software projects. The key issue in testing is determining the sufficiency of tests and, traditionally this has been done by using the Software Reliability Growth Models (SRGMs). However, SRGMs are not applicable when there are no stabilization phases as required by most software reliability models. Test-Defect Coverage Analytics Model (TDCAM) has been proposed to address this problem. This paper proposes the application of visual analytic techniques as an approach for supporting informed decision making in deciding the sufficiency of tests. Visual analytics research considers interactive visualization as the common platform for combining various computational data analysis techniques to support the analytical reasoning process. We presented four visual representations of TDCAM to demonstrate how analytical models have been applied to indicate the adequacy of tests in relation to sufficient and efficient test coverage. The techniques provide an effective and generally applicable test estimation on the basis of a general trend that higher test coverage correlates with higher probability of detecting more defects.

Multiple Software Reliability Growth Models (SRGM) has been illustrated to inspire designers and software developers to develop quality software. These models implicitly assume that expenditure is constant throughout testing effort. Recently importance was given to the testing phase by the development team and it requires controlled screening closely to raise its overall operational efficiency. Software Reliability Growth Models exhibits a precise relation between the failure patterns with time. Considering this as a basic building block a SRGM which is a Non-Homogeneous Poisson Process in nature is i in this work. The various aspects of our proposed model are testing effort function and fault detection rate (FDR). Mainly in Software Reliability Growth Model it was assumed that fault removal process are perfect which is not true in reality as new faults may get introduced during removal or some faults may not get removed due to its complexity. Thus in this model the concept of imperfect debugging was considered and assists us to justify the software reliability more convincingly through the help of testing effort function.

Software reliability growth model with change-point and environmental function

Management technologies for improving software reliability are very important for software TQM (Total Quality Management). The quality characteristics of software reliability is that computer systems can continue to operate regularly without the occurrence of failures on software systems. In this chapter, we describe several recent developments in software reliability modeling and its applications as quantitative techniques for software quality/reliability measurement and assessment. That is, a quality engineering analysis of human factors affecting software reliability during the design-review phase, which is the upper stream of software development, and software reliability growth models based on stochastic differential equations and discrete calculus during the testing phase, which is the lower one, are discussed. And, we discuss quality-oriented software management analysis by applying the multivariate analysis method and the existing software reliability growth models to actual process monitoring data. Finally, we investigate an operational performability evaluation model for the software-based system, introducing the concept of systemability which is defined as the reliability characteristic subject to the uncertainty of the field environment.KeywordsHuman factor analysisDesign-review experimentOSS reliabilityStochastic differential equationDiscrete modelingDifference equationSoftware managementSoftware project assessmentSoftware performability modelingSystemability assessment

Software is used in diverse settings and depends on development and testing environments. Software development should improve the reliability, quality, cost, and stability of software, making the software testing period crucial. We proposed a software reliability model (SRM) that considers the uncertainty of software environments and the fault detection rate function as a Rayleigh distribution, with an explicit mean value function solution in the model. The goodness-of-fit of the proposed model relative to several existing nonhomogeneous Poisson process (NHPP) SRMs is presented based on three software application failure datasets. Further, a cost model is also presented that addresses the error-removal risk level and required time. The optimal testing release policy for minimizing the expected total cost (ETC) is also determined for NHPP SRMs. The impact of the software environment is studied by varying it, and the optimal release times and minimum ETCs are compared. The goodness-of-fit comparison confirmed that the proposed model has more accurate prediction values than other models. Further, whereas the existing models applied to the cost model do not change after a certain operation period, the proposed model yields changes in release time even for long operating periods.

The software testing process basically aims at building confidence in the software for its use in real world applications. The reliability of a software system is always important to us. As we carry out the error detection and correction phenomenon on our software, the reliability of the software grows. With an aim to model this growth in the software reliability, many formulations in the form of Software Reliability Growth Models have been proposed. Many of these are based on Non-Homogeneous Poisson Process framework. In this paper, a parallel comparison of the performance of the proposed software reliability growth models is carried out, considering linear and exponential fault content functions for study of imperfect debugging situations. The performance of the proposed models has been compared with some famous existing software reliability models and the proposed models have been validated on some real-world datasets. Three goodness-of-fit criteria that include mean square error, predictive-ratio risk and predictive power are used to carry out the performance comparison of the models. Using these comparison criteria on six actual failure datasets, it is concluded that the proposed Model-2 which always outperforms Model-1, fits the actual failure data better and has better predictive power than other considered SRGMs for at least two data sets.

Software reliability assessment for an exponential-S-shaped reliability growth phenomenon

Owing to release of software in multiple releases, code changes take place in software. Because of this added complexity in software, the testing team may be unable to correct the fault upon detection, leaving the actual fault to reside in the software, termed as imperfect debugging or there may be replacement of original fault by other fault, leading to error generation. Many other factors exist that affect the testing phase of software like strategies of testing, test cases, skill, efficiency, and learning of testing team. All these factors cannot be kept stable during the whole process of testing. They may change at any time moment causing the background processes to experience change, which is known as change‐point. Keeping all these critical testing environment factors under consideration, a new software reliability growth model has been proposed, which is derived from an non homogenous Poisson process (NHPP)based unified scheme for multi‐release two‐stage fault detection/observation and correction/removal software reliability models. The developed model is numerically illustrated on tandem data set for four releases.