A Logistic Growth Model for Software Reliability Estimation Considering Uncertain Factors

2021 ◽  
pp. 2150032
Author(s):  
Md. Asraful Haque ◽  
Nesar Ahmad
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Estimation ◽  
Logistic Growth ◽  
Systematic Analysis ◽  
Software Reliability Growth ◽  
Logistic Growth Model ◽  
Operational Phase ◽  
Special Parameter

Software reliability growth models (SRGMs) are widely used to estimate software reliability by analyzing failure dataset throughout the testing process. A large number of SRGMs have been proposed on a regular basis by researchers since the 1970s. They are represented with a set of assumptions and a set of parameters. One major problem in SRGMs is that the uncertainties surrounding the assumptions and parameters are generally not taken into account by most of them. Therefore, sometimes, the predicted reliability on testing phase significantly varies in actual operational phase. This paper presents a logistic growth model that incorporates a special parameter to consider the effects of all possible uncertainties. A systematic analysis is carried out to identify the major uncertain factors and their impacts on the fault detection rate. The applicability of the model is shown by validating it on two different real datasets that are commonly used in various studies. The comparisons with nine established models in terms of mean square error (MSE), variance, predictive-ratio risk (PRR), [Formula: see text]and AIC have been presented.

ENHANCING SOFTWARE RELIABILITY OF A COMPLEX SOFTWARE SYSTEM ARCHITECTURE USING ARTIFICIAL NEURAL-NETWORKS ENSEMBLE

2011 ◽  
Vol 18 (03) ◽  
pp. 271-284 ◽  
Author(s):  
PARMOD KUMAR KAPUR ◽  
V. S. SARMA YADAVALLI ◽  
SUNIL KUMAR KHATRI ◽  
MASHAALLAH BASIRZADEH
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Estimation ◽  
Data Sets ◽  
Reliability Growth ◽  
Software Reliability Growth ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Neural Networks Ensemble

Modeling of software reliability has gained lot of importance in recent years. Use of software-critical applications has led to tremendous increase in amount of work being carried out in software reliability growth modeling. Number of analytic software reliability growth models (SRGM) exists in literature. They are based on some assumptions; however, none of them works well across different environments. The current software reliability literature is inconclusive as to which models and techniques are best, and some researchers believe that each organization needs to try several approaches to determine what works best for them. Data-driven artificial neural-network (ANN) based models, on other side, provide better software reliability estimation. In this paper we present a new dimension to build an ensemble of different ANN to improve the accuracy of estimation for complex software architectures. Model has been validated on two data sets cited from the literature. Results show fair improvement in forecasting software reliability over individual neural-network based models.

PROJECT RELIABILITY GROWTH MODEL BASED ON CURVES OF ACCUMULATED COMMUNICATION TOPICS FOR SOFTWARE DEVELOPMENT

2010 ◽  
Vol 20 (05) ◽  
pp. 665-677 ◽  
Author(s):  
NORIKO HANAKAWA
Keyword(s):  
Software Development ◽  
Growth Model ◽  
Software Reliability ◽  
Growth Models ◽  
Agile Software Development ◽  
Reliability Growth ◽  
Bug Reports ◽  
Detail Design ◽  
Software Reliability Growth ◽  
Agile Software

New trends in software development, such as agile software development, have a difficulty in conventional document-based management. Executable software has a higher priority than development documents such as detail design documents and formal bug reports. If managers depend on development reports in order to determine the project progress and the product quality, they will miss the opportunity of determining the progress and quality in agile software development. Therefore, we proposed a project reliability growth model for determining the project state without development documents. This model is based on conventional software reliability growth models. The parameters related to bugs are replaced with communication topic parameters. The concept and procedure of the model are the same as those of the software reliability growth model. By applying this model to open source projects, it is possible to detect a significant change in the project state without development documents.

SOFTWARE RELIABILITY GROWTH MODELING FRAMEWORKS WITH CHANGE OF TESTING-ENVIRONMENT

2011 ◽  
Vol 18 (04) ◽  
pp. 365-376 ◽  
Author(s):  
SHINJI INOUE ◽  
SHIGERU YAMADA
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Assessment ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Testing Environment ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Reliability Measurement

We discuss software reliability measurement with change of testing-environment by developing software reliability growth models. It is known that such change influences the accuracy for the software reliability assessment based on a software reliability growth model. This paper additionally shows numerical illustrations for software reliability measurement based on our software reliability growth models by using actual data.

The poststagnation stage for mature tourism areas

2016 ◽  
Vol 23 (2) ◽  
pp. 387-402 ◽  
Author(s):  
Isabel P. Albaladejo ◽  
María Pilar Martínez-García
Keyword(s):  
Life Cycle ◽  
Growth Model ◽  
Logistic Model ◽  
Temporal Evolution ◽  
Growth Models ◽  
Life Cycles ◽  
Logistic Growth ◽  
Tourism Sector ◽  
Passenger Flows ◽  
Logistic Growth Model

The tourism area life cycle (TALC) model of Butler explains the temporal evolution of a tourism resort. Lundtorp and Wanhill find that the logistic growth model represents the first phases of the TALC model. However, since the logistic model assumes a fixed tourism market ceiling, it fails to explain the poststagnation stage, where rejuvenation, decline, or any other intermediate possibility may arise. Taking into account the data of passenger flows to Bornholm from 1912 to 2001 collected by Lundtorp and Wanhill, the authors find that the superposition of several logistic growth models fits better with these data. Then they propose a multilogistic growth model, where investment or innovation in the tourism sector boosts the addition of new logistic curves which superpose the old ones. The continuous birth and superposition of these new life cycles is not free; it requires the purposive effort of entrepreneurs and governments seeking new markets and the improvement of infrastructures.

scholarly journals Logistic growth models of China pinks, cultivated on seven substrates, as a function of degree days

2016 ◽  
Vol 46 (11) ◽  
pp. 1924-1931
Author(s):  
Marília Milani ◽  
Sidinei José Lopes ◽  
Rogério Antônio Bellé ◽  
Fernanda Alice Antonello Londero Backes
Keyword(s):  
Growth Model ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Growth Models ◽  
Growth Period ◽  
Leaf Number ◽  
Logistic Growth ◽  
Maximum Height ◽  
Degree Days ◽  
Logistic Growth Model

ABSTRACT: The objective of this study was to characterize the height (H) and leaf number (LN) of China pinks, grown in seven substrates, as a function of degree days, using the logistic growth model. H and LN were measured from 56 plants per substrate, for 392 plants in total. Plants that were grown on substrates formed of 50% soil with 50% rice husk ash (50% S + 50% RH) and 80% rice husk ash with 20% worm castings (80% RH + 20% W) had the longest vegetative growth period (74d), corresponding to 1317.9ºCd. The logistic growth model, adjusted for H, showed differences in the estimation of maximum expected height (α) between the substrates, with values between 10.47cm for 50% S + 50% RH and 35.75cm for Mecplant(r). When α was estimated as LN, variation was also observed between the different substrates, from approximately 30 leaves on plants growing on 50% S + 50% RH to 34 leaves on the plants growing on the substrate formed of 80% RH + 20% W. Growth of China pinks can be characterized using H or LN in the logistic growth model as a function of degree days, being the provided plants adequately fertilized. The best substrates in terms of maximum height and leaf number were 80% soil + 20% worm castings and Mecplant(r). However, users must recalibrate the model with the estimated parameters before applying it to different growing conditions.

scholarly journals Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts

2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham
Keyword(s):  
Time Delay ◽  
Fault Detection ◽  
Software Reliability ◽  
Growth Models ◽  
Predictive Performance ◽  
Real Data ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Dual Processes ◽  
Correction Process

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

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