Software Reliability Growth Models Research Articles

Selecting Best Software Reliability Growth Models: A Social Spider Algorithm based Approach

Software Reliability is considered to be an essential part of software systems; it involves measuring the system's probability of having failures; therefore, it is strongly related to Software Quality. Software Reliability Growth Models are used to indicate the expected number of failures encountered after the software has been completed, it is also an indicator of the software readiness to be delivered. This paper presents a study of selecting the best Software Reliability Growth Model according to the dataset at hand. Several Comparison Criteria are used to yield a ranking methodology to be used in pointing out best models. The Social Spider Algorithm SSA, one of the newly introduced Swarm Intelligent Algorithms, is used for estimating the parameters of the SRGMs for two datasets. Results indicate that the use of SSA was efficient in assisting the process of criteria weighting to find the optimal model and the best overall ranking of employed models.

Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model

Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model

The Selection of Software Reliability Growth Models in Software Development Life Cycle

The Selection of Software Reliability Growth Models in Software Development Life Cycle

Optimal Release Time Estimation of Software System using Box-Cox Transformation and Neural Network

The determination of the software release time for a new software product is the most critical issue for designing and controlling software development processes. This paper presents an innovative technique to predict the optimal software release time using a neural network. In our approach, a three-layer perceptron neural network with multiple outputs is used, where the underlying software fault count data are transformed into the Gaussian data by means of the well-known Box-Cox power transformation. Then the prediction of the optimal software release time, which minimizes the expected software cost, is carried out using the neural network. Numerical examples with four actual software fault count data sets are presented, where we compare our approach with conventional Non-Homogeneous Poisson Process (NHPP) -based Software Reliability Growth Models (SRGMs).

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.

Design of Generalized Weighted Laplacian Based Quality Assessment for Software Reliability Growth Models

Design of Generalized Weighted Laplacian Based Quality Assessment for Software Reliability Growth Models

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.

A robust weighted SVR-based software reliability growth model

This paper proposes a new software reliability growth model (SRGM), which can be regarded as an extension of the non-parametric SRGMs using support vector regression to predict probability measures of time to software failure. The first novelty underlying the proposed model is the use of a set of weights instead of precise weights as done in the established non-parametric SRGMs, and to minimize the expected risk in the framework of robust decision making. The second novelty is the use of the intersection of two specific sets of weights, produced by the imprecise ε-contaminated model and by pairwise comparisons, respectively. The sets are chosen in accordance to intuitive conceptions concerning the software reliability behaviour during a debugging process. The proposed model is illustrated using several real data sets and it is compared to the standard non-parametric SRGM.

An SRGM for Multi-Release Open Source Software System

A number of software reliability growth models have been reported in the literature for open source software (OSS) systems but the effect of up-gradations on the reliability growth of multi-releases of such software systems has been discussed by a few. In this paper, the discrete modeling framework has been proposed to study the reliability growth process of OSS systems with multiple releases. The proposed model is based upon the assumption that during up-gradation some new faults are introduced in the code in addition to the left over fault content of the previous version. To validate our model, we have chosen two successful open source projects-Mozilla and Apache for its multi release failure datasets. Graphs representing goodness of fit of the proposed model have been drawn. The parameter estimates and measures of goodness of fit criteria suggest that the proposed software reliability growth model for multi-release OSS fits the actual datasets very well. An optimal release policy has been formulated by taking into account the cost of fault removal during testing and operational phases and reliability targets pre-specified by the decision makers. In addition, numerical example along with the sensitivity analysis has been provided to illustrate optimal release policy.

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.

Implementing weighted entropy-distance based approach for the selection of software reliability growth models

A computational quantitative model based on Weighted Euclidean Distance Based Approximation (WEBDA) has been developed to evaluate selection and rank the Software Reliability Growth Models (SRGMs) in ascending or descending order based on their Euclidean distance value from the optimal SRGM. The SRGM with Rank 1 is considered the optimal selection for the software developers on the particular dataset under consideration. The main problem of SRGMs selection and ranking is deployed as a multi-criteria decision making (MCDM) problem in which numerous inter-related attributes collectively termed as ranking criteria are identified to make the evaluation of available alternatives. In the present research, a data set from Tandem Computer Software Failure has been used to show the utility of developed model. Further the concept of methodology validation strengthens the present research by doing the comparison of obtained results with that of existing MCDM approaches such as Analytical Hierarchy Processing (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

Implementing weighted entropy-distance based approach for the selection of software reliability growth models

A computational quantitative model based on Weighted Euclidean Distance Based Approximation (WEBDA) has been developed to evaluate selection and rank the Software Reliability Growth Models (SRGMs) in ascending or descending order based on their Euclidean distance value from the optimal SRGM. The SRGM with Rank 1 is considered the optimal selection for the software developers on the particular dataset under consideration. The main problem of SRGMs selection and ranking is deployed as a multi-criteria decision making (MCDM) problem in which numerous inter-related attributes collectively termed as ranking criteria are identified to make the evaluation of available alternatives. In the present research, a data set from Tandem Computer Software Failure has been used to show the utility of developed model. Further the concept of methodology validation strengthens the present research by doing the comparison of obtained results with that of existing MCDM approaches such as Analytical Hierarchy Processing (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

A Discrete SRGM for Multi-Release Software System with Faults of Different Severity

To meet highly competitive market challenges along with technological upgrades and changing user requirements, software firms offer newer versions of their products by adding new features and new functionalities. But this also results in increase in the fault content of software. In this paper we propose a discrete software reliability growth model (SRGM) for fault removal process incorporating the effect of up-gradations on the subsequent releases of the software. It is further assumed that faults present in the software are not of the same type and may be classified as simple and hard faults depending upon the effort and time consumed for their removal. The proposed model has been validated on a real life dataset for software with four releases. The results obtained are encouraging and fairly accurate.

Generalised framework for optimal pre and post release software testing in presence of warranty

Significant advances in the field of software testing have been made by providing extended warranties, operational support and desired software reliability to the customers. However, the trade-off between minimising testing cost and maximising reliability to ensure well-timed market penetration has been a key concern for developers. Separate release and testing stop time strategy in order to optimise overall testing and market opportunity cost is proposed. We propose a generalised framework for developers to achieve multiple objectives of minimum testing and market opportunity cost; optimal warranty length; and optimal testing and release time. Software reliability growth models (SRGMs) are used to model an average number of bugs detected by testers (users) during the pre (post) release of software. Significant implications and quite intriguing facts related to separate release and testing stop time; early release ensuring higher reliability and; capitalising market opportunity to minimise overall testing cost are suggested based on proposed optimisation models. Numerical illustration is provided to validate the proposed framework.

On allocation of resources during testing phase incorporating flexible Software Reliability Growth Model with testing effort under Dynamic Environment

We develop an optimal control model for optimally allocating effort between detection and correction processes, during testing phase of a software development life cycle (SDLC). The objective is to propose a resource allocation plan to minimise the total testing cost with the reliability criteria using flexible software reliability growth model considering testing effort under dynamic environment. Furthermore, we have incorporated the learning curve effect in correction cost function due to its importance in cost estimation, and resource allocation decisions. A technique based on optimal control theory is proposed for the software managers to optimise the allocation of the limited resources, while genetic algorithm is used to find the approximate value of detection and correction effort. An illustrative example is presented. The optimal release policy under reliability criteria is also discussed.

A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal

Most existing software reliability growth models (SGRMs) often assume software faults are mutually independent and the detected faults can be perfectly removed. However, those two assumptions are not realistic in practice since the dependent faults can also exist in the program. At the same time, it is unlikely to correct all the detected faults in the testing phase due to the limitation of testing resource, the skill and experience of the programmer, and multi-release consideration for software organization. This paper presents a new non-homogeneous Poisson process (NHPP) software reliability model with a pioneering idea by considering software fault dependency and imperfect fault removal. In order to clearly explain software fault dependency, some facts and examples are discussed in Section 1. Two types of software faults are defined, Type I (independent) fault and Type II (dependent) fault, according to fault dependency. Two phases debugging processes, Phase I and Phase II, are proposed according to the debugged software fault type. A small portion of software faults that software testers are not able to remove is also considered in both phases in the proposed model. The illustration of the model effectiveness is based on the three datasets collected from industries. Some limitations of the proposed model are also discussed in the last section.

Software reliability growth modeling for agile software development

Abstract The frequent incremental release of software in agile development impacts the overall reliability of the product. In this paper, we propose a generic software reliability model for the agile process, taking permanent and transient faults into consideration. The proposed model is implemented using the NHPP (non-homogenous Poisson process) and the Musa model. The comparison of the two implementations yields an effective, empirical and reliable model for agile software development.

Reliability over consecutive releases of a semiconductor Optical Endpoint Detection software system developed in a small company

Demonstrating software reliability across multiple software releases has become essential in making informed decisions of upgrading software releases without impacting significantly end users’ characterized processes and software quality standards. Standard defect and workload data normally collected in a typical small software development organization can be used for this purpose. Most of these organizations are normally under aggressive schedules with limited resources and data availability that are significantly different from large commercial software organizations where software reliability engineering has been successfully applied. Trend test, input domain reliability models (IDRM), and software reliability growth models (SRGM) were used in this paper on a semiconductor Optical Endpoint Detection (OED) software system to examine its overall trend and stability, to assess the system’s operational reliability, and to track its reliability growth over multiple releases. These techniques also provided evidence that continuous defect fixes increased software reliability substantially over time for this software system.

Efficient parameter estimation of software reliability growth models using harmony search

The primary challenge of software reliability growth model is to find the unknown model parameters that are used to validate on software failure dataset. Though, numerical estimation technique plays a vital role in parameter estimation of software reliability growth models, they are not optimal as they suffer from constraints sucha as sample size, biasing, and initialisation of parameters. In this study, a parameter estimation of software reliability growth model that utilises a variant of harmony search is proposed. Extensive experiments are conducted on seven different software datasets of varying complexity. A robust experimental setup is developed employing an orthogonal array and Taguchi method. Two-fold performance comparisons are performed. First, the authors tested their proposed approach against Cuckoo search and numerical method (least square estimation) considering mean square error and Theil's statistics as a quality measure. Second, the authors applied statistical tests are performed that demonstrate the superiority of their approach over the others. The underlying motivation to conduct this study is to motivate researchers to utilise their approach for a better estimation of model parameters.

Optimal Allocation Based on Cost, Testing Effort and Reliability

In this paper, we investigate a software resource allocation problem. We incorporate generalized modified weibull testing effort function in software reliability growth model to get a better description of the software debugging process. The main purpose is to minimize the cost of software development when the number of remaining faults and a desired reliability objective are given. A real software failure project is demonstrated the effectiveness of proposed models.