Imperfect Debugging Research Articles

A Generalized Software Reliability Growth Model With Consideration of the Uncertainty of Operating Environments

This paper proposes a generalized model to cover imperfect debugging and the uncertainty of the operating environment and its effect on fault detection rate into software reliability evaluation based on a non-homogeneous Poisson process (NHPP). Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures over the past 40 years, but most of these models assume that the operating environment is the same as the testing environment. However, in fact, due to the unpredictability of the uncertain factors in the operating environments for the software, they may considerably influence the software's reliability in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the original reliability prediction in the testing phase of the software development process, also from all its similar applications in other fields. In this paper, a general model is used to derive models that incorporate the uncertainty of operating environments, which provides the flexibility in considering a different fault detection rate and random environmental factor and so on. Several published models are shown to be covered by this general model and a new model is also developed and examined. The numerical illustrative examples of the proposed model have been validated on two sets of real software failure data in terms of six criteria. The comparison results demonstrate that the new model can fit and predict significantly better than other existing models.

A unified approach of testing coverage‐based software reliability growth modelling with fault detection probability, imperfect debugging, and change point

AbstractThis paper presents a unified approach to model the reliability growth of software with imperfect debugging and coverage factor. Existing testing coverage‐based software reliability growth models considered that faults present at a particular fault location are detected with certainty during the testing process. Practically, it is very difficult to detect all software faults. To overcome this limitation, a revised software reliability growth model has been developed with the assumption that detection of the faults at a particular fault location is not definite. Furthermore, a new method to model the imperfect debugging phenomenon has been incorporated in the proposed study. A revised model ranking method has been developed to improve the accuracy of model ranking, which is mainly extension of existing normalized criteria distance method. Change point analysis has been done with the effect of different environmental factors on the models' parameters. Numerical examples are given to demonstrate the effectiveness of the proposed model.

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.

2-Dimensional Multi-Release Software Reliability Modelling considering Fault Reduction Factor under imperfect debugging

Introduction: The present research was conducted at the University of Delhi, India in 2017.
 Methods: We develop a software reliability growth model to assess the reliability of software products released in multiple versions under limited availability of resources and time. The Fault Reduction Factor (frf) is considered to be constant in imperfect debugging environments while the rate of fault removal is given by Delayed S-Shaped model.
 Results: The proposed model has been validated on a real life four-release dataset by carrying out goodness of fit analysis. Laplace trend analysis was also conducted to judge the trend exhibited by data with respect to change in the system’s reliability.
 Conclusions: A number of comparison criteria have been calculated to evaluate the performance of the proposed model relative to only time-based multi-release Software Reliability Growth Model (srgm).
 Originality: In general, the number of faults removed is not the same as the number of failures experienced in given time intervals, so the inclusion of frf in the model makes it better and more realistic. A paradigm shift has been observed in software development from single release to multi release platform.
 Limitations: The proposed model can be used by software developers to take decisions regarding the release time for different versions, by either minimizing the development cost or maximizing the reliability and determining the warranty policies.

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

SummaryThis paper propose a learning algorithm of supervised back‐propagation neural networks for dynamic weighted combination of software reliability model. The proposed model is an assimilation of 3 well‐known non‐homogeneous poisson process (NHPP)–based software reliability growth models with imperfect debugging. The novel approach of proposed supervised back propagation–based neural network 2‐stage architecture has a great impact on the network by combining the imperfect debugging models based on the nature of fault introduction rate during testing and debugging. Function approximation metrics are used for comparing the proposed model with individual models. Three data sets are trained using supervised back‐propagation neural networks to compare the performance and validity evaluation of proposed and existing NHPP models and dynamic weighted combinational model. Reliability analysis among important NHPP models incorporating imperfect debugging is illustrated through numerical and graphical explanation of several metrics using supervised back‐propagation neural networks.

Fault Prediction Modelling in Open Source Software Under Imperfect Debugging and Change-Point

Instant demand of products and services by technologically active users has increased the demand for open source software (OSS)-based applications. Unfortunately, with the complexity and lack of understanding of OSS-based systems, it becomes difficult for a testing team to remove the faults and the fault removal rate becomes low in comparison to what it should be. This also results in generating new faults during removal. Also, the rate at which the testing team detects/corrects fault need not be same during the entire process of testing due to various reasons viz. change in testing strategy, understanding of code, change in resources, etc. In the existing literature on OSS, authors have developed many models considering the above aspects separately. In this article, all of the above aspects have been combined to develop a general framework for predicting the number of faults in OSS. The comparison of eight models on the basis of their prediction capability on two well-known Open Source Software datasets is created and then ranked using normalized criteria distance approach.

Markovian Software Reliability Modeling with Change-Point

We discuss a Markovian modeling approach for software reliability assessment with the effects of change-point and imperfect debugging environment. Testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably is called change-point. Taking into account the effect at change-point in software reliability growth modeling is important to improve the accuracy of software reliability assessment. Our modeling approach describes a software reliability growth process with not only the effect of change-point but also the imperfect debugging activities based on a semi-Markov process for reflecting actual situation of debugging activities. Finally, we show numerical examples of our model for software reliability analysis and check the performance of our model with an existing Markovian software reliability growth model by using actual data.

Reliability analysis of software with three types of errors and imperfect debugging using Markov model

In this analysis, we consider a Markovian software reliability model (SRM) with imperfect debugging (ID) wherein software may fail due to three types of errors called error generation (EG). For developing the governing differential equations of SRM with three types of errors, an irreducible Markov process is used. Further, we have applied the method namely 'Runge-Kutta' (R-K) under transient condition for obtaining reliability of concerned system in different configurations. Moreover, we suggest various software reliability indices like reliability, failure frequency and mean time to failure (MTTF). By taking a numerical illustration, the sensitivity analysis is done to demonstrate the validity of analytical outcomes and to explore the effects of various parameters. Moreover, the analytical results are also compared with adaptive neuro-fuzzy inference system (ANFIS). At last, conclusions are given.

Reliability analysis of software with three types of errors and imperfect debugging using Markov model

In this analysis, we consider a Markovian software reliability model (SRM) with imperfect debugging (ID) wherein software may fail due to three types of errors called error generation (EG). For developing the governing differential equations of SRM with three types of errors, an irreducible Markov process is used. Further, we have applied the method namely 'Runge-Kutta' (R-K) under transient condition for obtaining reliability of concerned system in different configurations. Moreover, we suggest various software reliability indices like reliability, failure frequency and mean time to failure (MTTF). By taking a numerical illustration, the sensitivity analysis is done to demonstrate the validity of analytical outcomes and to explore the effects of various parameters. Moreover, the analytical results are also compared with adaptive neuro-fuzzy inference system (ANFIS). At last, conclusions are given.

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.

NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

• The uncertainty of operating environments and imperfect debugging are considered. • Fault detection rate is based on testing coverage and the model is based on NHPP. • The performance of the proposed model is compared with other 15 existing models. • Optimal software release time and its sensitivity analysis are discussed based on cost and reliability. • Seven criteria and improved normalized criteria distance method are used in the experiments. In this paper, we propose a testing-coverage software reliability model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP). Software is usually tested in a given control environment, but it may be used in different operating environments by different users, which are unknown to the developers. Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures, but most of the underlying common assumptions of these models are that the operating environment is the same as the developing environment. But in fact, due to the unpredictability of the uncertainty in the operating environments for the software, environments may considerably influence the reliability and software's performance in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the theory reliability, and also from all its similar applications in other fields. In this paper, a new model is proposed with the consideration of the fault detection rate based on the testing coverage and examined to cover ID subject to the uncertainty of operating environments. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real software failure data based on seven criteria. Improved normalized criteria distance (NCD) method is also used to rank and select the best model in the context of a set of goodness-of-fit criteria taken all together. All results demonstrate that the new model can give a significant improved goodness-of-fit and predictive performance. Finally, the optimal software release time based on cost and reliability requirement and its sensitivity analysis are discussed.

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.

An imperfect software debugging model considering irregular fluctuation of fault introduction rate

ABSTRACTIn general, software testing is a complicated and uncertain process. New faults can be introduced into the software during each fault removal. This process is called imperfect debugging. For simplicity, fault introduction rates are generally assumed to be constant. However, software debugging can be affected by many factors, such as subjective and objective influences, the difficulty and complexity of fault removal, the dependent relationships among faults, the changes in different phases of software testing, and the test schedules. Thus, the rate of fault introduction is not a constant, but is an irregularly fluctuating variable in software debugging. In this article, we propose a model with imperfect software debugging considering the irregular changes in fault introduction rates during software debugging. Experimental results reveal that our proposed model has good fitting capability and considerably stronger forecasting performance than that of the other models, and that the proposed model assumptions are close to the actual software debugging situation. Moreover, research on the irregular fluctuation of the fault introduction rate in software debugging has a certain reference value and important significance for software-intensive product testing, for instance, cloud computing.

A Multi Release Cost Model in Distributed Environment

Software industry has reached far beyond since its origin. Technical advancements are taking place at a speed faster than ever. This has further enhanced the pressure on software developers. They are trying hard to keep a pace with these rapid developments by coming out with strategies to increase the pace of their work without significantly affecting the software quality and reliability. One such strategy is distributed development of software in which the software is composed of two different types of components viz. newly developed and re-used component. Software developed in Distributed Development Environment (DDE) is characterized by enhanced availability and reliability. At the same time, the ever growing contention in the market and increasing needs of customers obligate the developers to come out with enhanced functionalities in the software from time to time leading to multiple releases of a software. The added functionalities further enhance the existing complexity of the software and testing team may not be able to perfectly remove the faults leading to imperfect debugging or add more bugs due to lack of knowledge about the software in the initial phase known as error generation. In this paper, we incorporated this real-life phenomenon to come out with a multi up-gradations modeling for removal of software fault in a distributed environment. In the present framework we described a cost model to obtain optimal release time in multi up-gradation of software under distributed environment. To validate the analytical results of the proposed framework, numerical illustration is provided.

Software reliability growth models: A comparison of linear and exponential fault content functions for study of imperfect debugging situations

AbstractThe 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-rat...

Imperfect Debugging Software Reliability Growth Model for the Rayleigh failure time distribution

Imperfect Debugging Software Reliability Growth Model for the Rayleigh failure time distribution

Change point–based software reliability model under imperfect debugging with revised concept of fault dependency

A detailed study about the characteristics of different types of faults is necessary to enhance the accuracy of software reliability estimation. Over the last three decades, some software reliability growth models have been proposed considering the possibility of existence of two types of faults in a software: (1) independent and (2) dependent faults. In these software reliability growth models, it is considered that the removal of a leading fault or independent fault causes detection of corresponding dependent faults. In practical, it is noticed that some dependent faults are possible in a software which are removed during the removal of other faults. Moreover, dependent faults may have different characteristics, which cannot be ignored. Considering these facts, a detailed study about the different characteristics of both dependent and independent faults has been performed, and based on this study, dependent faults have been categorized into different categories. Furthermore, a new software reliability growth model has been proposed with revised concept of fault dependency under imperfect debugging by introducing the fault removal proportionality. In addition, the effect of change point on model’s parameters due to different environmental factors has been considered. The fault reduction factor is considered as a proportionality function. Experimental results establish the fact that the performance of the proposed model is better with respect to estimated and predicted cumulative number of faults on some real software failure datasets.

A New Method to Optimize the Reliability of Software Reliability Growth Models using Modified Genetic Swarm Optimization

reliability is one of the key attributes to determine the quality of a software system. Finding and minimizing the remaining faults in software systems is a challenging task. Software reliability growth model (SRGM) with testing-effort function (TEF) is very helpful for software developers and has been widely accepted and applied. However, each SRGM with TEF (SRGMTEF) contains some undetermined parameters. Optimization of these parameters is a necessary task. Generally, these parameters are estimated by the Least Square Estimation (LSE) or the Maximum Likelihood Estimation (MLE). However, the software failure data may not satisfy such a distribution. We investigate the improvement and application of a swarm intelligent optimization algorithm, namely Modified Genetic Swarm Optimization algorithm, to optimize these parameters of SRGMTEF. The performance of the proposed SRGMTEF model with optimized parameters is also compared with other existing models Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The experiment results show that the proposed parameter optimization approach using Modified Genetic Swarm Optimization is very effective and flexible, and the better software reliability growth performance can be obtained based on SRGMTEF on the different software failure datasets. Also, provided comparison of ten SRGMs ( Like Goel-Okumoto Model, Delayed S-shaped Growth Model, Yamada Imperfect Debugging Models, Yamada Rayleigh Model, Inflection S-shaped Model…..etc).

Cost Optimization of SRGM using Genetic Algorithm

applications of computer systems have been increased immensely during the last few decades and the system reliability is major concern which is depends upon reliability of software and hardware components. Software testing is quality assurance process which confirms that the product is error free and reliable. The reliability of software is major quality attribute which ensure failure free operations and maintainability, therefore reliability assessment is necessary. Software reliability control the optimal release time and cost of software development. In this paper various fault detection and removal strategies are discussed to increase the reliability. A software reliability growth model with imperfect debugging based on Non-homogenous Position Process (NHPP) model is incorporated. The reliability estimation is based on testing and operational reliability of systems. The various numerical parameters are examined and results are presented with the GA tool of MATLAB for optimal release policy based on cost and reliability criterion.

Modeling and Analysis of Software Fault Detection and Correction Process Through Weibull-Type Fault Reduction Factor, Change Point and Imperfect Debugging

Fault reduction factor (FRF) is one of the most important factors which plays a vital role in software reliability growth. In the past, few studies on the influence of different environmental factors into FRF have been carried out. In these studies, FRF has been defined using some particular functions such as constant, increasing, decreasing and inflection S-shaped. These functions may not be realistic and reasonable to represent the actual behavior of FRF. Therefore, in this study, it has been tried to represent the realistic behavior of FRF using Weibull curve. Moreover, a new approach of software reliability modeling has been proposed in which FRF has been incorporated in fault detection and correction process. Thus, in this paper, a general frame work of software reliability growth model (SRGM) has been proposed considering the fault detection and correction process. The concepts of imperfect debugging and change point have also been incorporated in the present study. Different parameters of the proposed SRGM are estimated using the SPSS and ‘R’ software. Different comparison criteria have been used for comparison of the proposed SRGM with other existing SRGMs. Chi-square goodness-of-fit test has been used for validation of the proposed SRGM.