Multi Release Software Reliability Research Articles

Multi-release software reliability assessment: testing coverage-based approach

Software development companies continue to improve their products to stay up with the market's growing needs by adding new features and fixing previously identified bugs. Software reliability growth models (SRGMs) with testing effort function are the incredibly valuable and have been widely utilised by software engineers. Many researchers have built SRGMs that incorporate the concept testing coverage in the model building for the multi-release software system. The proposed model intends to give three different models of multi-release software reliability modelling with testing coverage function. Three models describe testing coverage by exponential function, delayed S-shaped function and logistic function respectively, with the testing effort function assumed to be Weibull in nature. Real-world data is used to estimate parameters in order to validate the proposed SRGM of four releases from Tandem Computers and the model goodness-of-fit is assessed. The results suggest that the proposed model matches the failure data effectively.

Multi-Release Software Reliability Assessment: Testing Coverage Based Approach

Multi-Release Software Reliability Assessment: Testing Coverage Based Approach

Multi-release software reliability growth assessment: an approach incorporating fault reduction factor and imperfect debugging

Multi-release software reliability growth assessment: an approach incorporating fault reduction factor and imperfect debugging

Multi-Release Software Reliability Growth Assessment: An approach incorporating Fault Reduction Factor and Imperfect Debugging

In the last few decades, a plenty of software reliability growth models with diverse parameters have been formulated and proposed to assess software trustworthiness. Fault reduction factor (FRF) is one such significant factor which has been incorporated in numerous reliability modelling researches in past and results have proved its importance on reliability growth phenomenon. To keep pace with rising demands of the market, software development firms keep upgrading their products in terms of new features and by providing rectification of previously reported defects. In this paper, an SRGM based on NHPP integrating imperfect debugging and time-variable FRF is proposed for multi-release software systems. The model is then tested on the failure datasets of multiple releases of Tandem Computers and Firefox OSS. The experimental results for each release are compared with their corresponding perfect debugging model. Results illustrate that the proposed model fits the failure data well, outperforming perfect debugging model.

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 multi-release software reliability modeling for open source software incorporating dependent fault detection process

The increasing dependence of our modern society on software systems has driven the development of software products become even more competitive and time-consuming. Single release software product no longer meets the increasing market requirements. Thereby it is important to release multiple version software products in order to add new features in the next release and fix remaining faults from previous release. In this paper, we develop a multi-release software reliability model with consideration of the remaining software faults from previous release and the new introduced-faults (from newly added features). Additionally, dependent fault detection process is taken into account in this research. In particular, the detection of a new fault for developing the next release depends on the detection of the remaining faults from previous release and the detection of the new introduced-faults. The proposed model is validated on the open source software project datasets with multiple releases.

An NHPP SRGM with Change Point and Multiple Releases

In an environment of intense competition, software upgrades have become the necessity for the survival in software industry. In this paper, the authors propose a discrete Software Reliability Growth Model (SRGM) for the software with successive releases by taking into consideration the realistic assumption that Fault Removal Rate (FRR) may not remain constant during the testing process, it changes due to severity of faults detected and due to change in strategies adapted by testing team and the time point where FRR changes is called the Change Point. Many researchers have developed SRGMs incorporating the concept of Change Point for single release software. The proposed model aims to present multi release software reliability modeling with change point. Discrete logistic distribution function has been used to model relationship between features enhancement and fault removal. It is helpul in developing a flexible SRGM, which is S-shaped in nature. In order to evaluate the proposed SRGM, parameter estimation is done using the real life data set for software with four releases and the goodness-of-fit of this model is analyzed.

Two-Dimensional Multi-Release Software Reliability Modeling for Fault Detection and Fault Correction Processes

With the growing competition and the demand of the customers, a software organization needs to regularly provide up-gradations and add features to its existing version of software. For the organization, creating these software upgrades means an increase in the complexity of the software which in turn leads to the increase in the number of faults. Also, the faults left undetected in the previous version need to be addressed in this phase. Many software reliability growth models have been proposed to model the phenomenon of multi-release problems using two stage failure observation and correction processes. The model proposed in this paper partitions the fault removal process into a two-stage process which includes fault detection process and fault removal process considering the joint effect of premeditated release pressure and resource restrictions using a well-known Cobb–Douglas production function for the multi release problem of a software. The faults detected in the operational phase of the previous release or left incomplete are also incorporated in the next release. A generalized framework for the multi-release problem in which fault detection follows an exponential distribution function and fault correction follows Gamma distribution function is proposed and verified on a real data set of four releases of software. The estimated parameters and comparison criteria are also given.

Fault severity based multi-release SRGM with testing resources

In today’s environment of global competition where each company is trying to prove itself better than its competitors the software developers have to come up with multiple releases in order to survive in the market. Each release offers some innovative performance enhancement or some new functionality that distinguishing itself from the past release. But enhancing the product and upcoming with successive releases puts a constant pressure on even the best organized engineering organizations. The reason being, up-grading a software application is a complex process. Upgrading a software introduces the risk that the new release will contain a bug, causing the program to fail. Therefore, to capture the effect of faults generated in the software with multiple releases, we have developed a multi release software reliability model in this paper. The model uniquely takes into account the faults of the current release and the remaining faults of just previous release. The multi release software reliability growth model treats the fault removal rate as a function of testing resources consumed. In addition, the model also incorporates the severity of faults and considers that hard faults are removed with different rate than the rate required to remove simple faults. The model developed is validated on real data set with software which has been released in the market with new features four times.

Two Dimensional Multi-Release Software Reliability Modeling and Optimal Release Planning

Long-lived software systems evolve through new product releases, which involve up-gradation of previous released versions of the software in the market. But, upgrades in software lead to an increase in the fault content. Thus, for modeling the reliability growth of software with multiple releases, we must consider the failures of the upcoming upgraded release, and the failures that were not debugged in the previous release. Based on this idea, this paper proposes a mathematical modeling framework for multiple releases of software products. The proposed model takes into consideration the combined effect of schedule pressure and resource limitations using a Cobb Douglas production function in modeling the failure process using a software reliability growth model. The model developed is validated on a four release failure data set. Another major concern for the software development firms is to plan the release of the upgraded version. When different versions of the software are to be released, then the firm plans the release on the basis of testing progress of the new code, as well as the bugs reported during the operational phase of the previous version. In this paper, we formulate an optimal release planning problem which minimizes the cost of testing of the release that is to be brought into market under the constraint of removing a desired proportion of faults from the current release. The problem is illustrated using a numerical example, and is solved using a genetic algorithm.

Multi up-gradation software reliability growth model with imperfect debugging

Due to demand of new features and highly reliable software system, the software industries are speeding their up-gradations/add-ons in the software. The life of software is very short in the environment of perfect competition. Therefore the software developers have to come up with successive up gradations to survive. The reported bugs from the existing software and Features added to the software at frequent time intervals lead to complexity in the software system and add to the number of faults in the software. The developer of the software can lose on market share if it neglects the reported bugs and up gradation in the software and on the other hand a software company can lose its name and goodwill in the market if the reported bugs and functionalities added to the software lead to an increase in the number of faults in the software. To capture the effect of faults due to existing software and generated in the software due to add-ons at various points in time, we develop a multi up-gradation, multi release software reliability model. This model uniquely identifies the faults left in the software when it is in operational phase during the testing of the new code i.e. developed while adding new features to the existing software. Due to complexity and incomplete understanding of the software, the testing team may not be able to remove/correct the fault perfectly on observation/detection of a failure and the original fault may remain resulting in the phenomenon known as imperfect debugging, or get replaced by another fault causing error generation The model developed is validated on real data sets with software which has been released in the market with new features four times.