Software Release Time Management: How to Use Reliability Growth Models to Make Better Decisions

2006 ◽  
Author(s):  
Chu-ti Lin ◽  
Chin-yu Huang
Keyword(s):  
Time Management ◽  
Growth Models ◽  
Release Time ◽  
Reliability Growth ◽  
Software Release

scholarly journals Determining Software Time-to-Market and Testing Stop Time when Release Time is a Change-Point

2020 ◽  
Vol 5 (2) ◽  
pp. 208-224 ◽  
Author(s):  
Ompal Singh ◽  
Saurabh Panwar ◽  
P. K. Kapur
Keyword(s):  
Change Point ◽  
Growth Models ◽  
Release Time ◽  
Optimal Time ◽  
Software Project ◽  
Reliability Growth ◽  
Numerical Illustration ◽  
Time To Market ◽  
Software Product ◽  
Software Release

In software engineering literature, numerous software reliability growth models have been designed to evaluate and predict the reliability of the software products and to measure the optimal time-to-market of the software systems. Most existing studies on software release time assessment assumes that when software is released, its testing process is terminated. In practice, however, the testing team releases the software product first and continues the testing process for an added period in the operational phase. Therefore, in this study, a coherent reliability growth model is developed to predict the expected reliability of the software product. The debugging process is considered imperfect as new faults can be introduced into the software during each fault removal. The proposed model assumes that the fault observation rate of the testing team modifies after the software release. The release time of the software is therefore regarded as the change-point. It has been established that the veracity of the performance of the growth models escalates by incorporating the change-point theory. A unified approach is utilized to model the debugging process wherein both testers and users simultaneously identify the faults in the post-release testing phase. A joint optimization problem is formulated based on the two decision criteria: cost and reliability. In order to assimilate the manager’s preferences over these two criteria, a multi-criteria decision-making technique known as multi-attribute utility theory is employed. A numerical illustration is further presented by using actual data sets from the software project to determine the optimal software time-to-market and testing termination time.

Optimal Software Release Policies with Debugging Time Lag

1997 ◽  
Vol 04 (03) ◽  
pp. 241-255 ◽  
Author(s):  
Tadashi Dohi ◽  
Naoto Kaio ◽  
Shunji Osaki
Keyword(s):  
Error Detection ◽  
Software Maintenance ◽  
Growth Models ◽  
Time Lag ◽  
Computer Software ◽  
Release Time ◽  
Software Release ◽  
Software Reliability Growth ◽  
Operation Phase ◽  
Non Homogeneous Poisson Process

This paper presents a new stochastic model for determining the optimal release time for a computer software in testing phase, taking account of the debugging time lag. In the earlier works, most of software release models were considered, but it was assumed that an error detected can be removed instantaneously. In other words, none discussed quantitatively the effect of the software maintenance action in the optimal software release time. Main purpose of this work is to relate the optimal software release policy with the arrival-service process on the software operation phase by users. We use the Non-Homogeneous Poisson Process (NHPP) type of software reliability growth models as the software error detection phenomena and obtain the optimal software release policies minimizing the expected total software costs. As a result, the usage circumstance of a software in operation phase gives a monotone effect to the software release planning.

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

2018 ◽  
Vol 3 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Momotaz Begum ◽  
Tadashi Dohi
Keyword(s):  
Neural Network ◽  
Count Data ◽  
Growth Models ◽  
Time Estimation ◽  
Critical Issue ◽  
Release Time ◽  
Data Sets ◽  
Software Cost ◽  
Software Release ◽  
Software Fault

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).

Key Issues in Software Reliability Growth Models

2020 ◽  
Vol 13 ◽  
Author(s):  
Md. Asraful Haque ◽  
Nesar Ahmad
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Poisson Model ◽  
Release Time ◽  
Reliability Growth ◽  
Affecting Factors ◽  
Mathematical Basis ◽  
Data Set ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

Background: Software Reliability Growth Models (SRGMs) are most widely used mathematical models to monitor, predict and assess the software reliability. They play an important role in industries to estimate the release time of a software product. Since 1970s, researchers have suggested a large number of SRGMs to forecast software reliability based on certain assumptions. They all have explained how the system reliability changes over time by analyzing failure data set throughout the testing process. However, none of the models is universally accepted and can be used for all kinds of software. Objective: The objective of this paper is to highlight the limitations of SRGMs and to suggest a novel approach towards the improvement. Method: We have presented the mathematical basis, parameters and assumptions of software reliability model and analyzed five popular models namely Jelinski-Moranda (J-M) Model, Goel Okumoto NHPP Model, Musa-Okumoto Log Poisson Model, Gompertz Model and Enhanced NHPP Model. Conclusion: The paper focuses on the many challenges like flexibility issues, assumptions, and uncertainty factors of using SRGMs. It emphasizes considering all affecting factors in reliability calculation. A possible approach has been mentioned at the end of the paper.

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

2021 ◽  
pp. 1748006X2110337
Author(s):  
Vishal Pradhan ◽  
Ajay Kumar ◽  
Joydip Dhar
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Time Lag ◽  
Reduction Factor ◽  
Release Time ◽  
Data Sets ◽  
Time Lags ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Software Reliability Growth

The fault reduction factor (FRF) is a significant parameter for controlling the software reliability growth. It is the ratio of net fault correction to the number of failures encountered. In literature, many factors affect the behaviour of FRF, namely fault dependency, debugging time-lag, human learning behaviour and imperfect debugging. Besides this, several distributions, for example, inflection S-shaped, Weibull and Exponentiated-Weibull, are used as FRF. However, these standard distributions are not flexible to describe the observed behaviour of FRFs. This paper proposes three different software reliability growth models (SRGMs), which incorporate a three-parameter generalized inflection S-shaped (GISS) distribution as FRF. To model realistic SRGMs, time lags between fault detection and fault correction processes are also incorporated. This study proposed two models for the single release, whereas the third model is designed for multi-release software. Moreover, the first model is in perfect debugging, while the rest of the two are in an imperfect debugging environment. The extensive experiments are conducted for the proposed models with six single release and one multi-release data-sets. The choice of GISS distribution as an FRF improves the software reliability evaluation in comparison with the existing systems in the literature. Finally, the development cost and optimal release time are calculated in a perfect debugging environment.

Optimum bug fixing rate and bug fixing time detection by software reliability modelling

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rama Rao Narvaneni ◽  
K. Suresh Babu
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Mathematical Optimization ◽  
Release Time ◽  
Software Project ◽  
Reliability Growth ◽  
Content Type ◽  
Bug Fixing ◽  
Software Reliability Growth

PurposeSoftware reliability growth models (SRGMs) are used to assess and predict reliability of a software system. Many of these models are effective in predicting future failures unless the software evolves.Design/methodology/approachThis objective of this paper is to identify the best path for rectifying the BFT (bug fixing time) and BFR (bug fixing rate). Moreover, the flexible software project has been examined while materializing the BFR. To enhance the BFR, the traceability of bug is lessened by the version tag virtue in every software deliverable component. The release time of software build is optimized with the utilization of mathematical optimization mechanisms like ‘software reliability growth’ and ‘non-homogeneous Poisson process methods.’FindingsIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.Originality/valueIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.

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