Conversion method from fault detection rate to fault test coverage rate

2021 ◽  
Author(s):  
Yi Deng ◽  
Junyou Shi ◽  
Zhenyang Lv
Keyword(s):  
Fault Detection ◽  
Detection Rate ◽  
Coverage Rate ◽  
Test Coverage ◽  
Conversion Method

scholarly journals NHPP Software Reliability Model with Inflection Factor of the Fault Detection Rate Considering the Uncertainty of Software Operating Environments and Predictive Analysis

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 521 ◽  
Author(s):  
Song ◽  
Chang ◽  
Pham
Keyword(s):  
Fault Detection ◽  
Software Reliability ◽  
Detection Rate ◽  
The Other ◽  
Reliability Model ◽  
New Model ◽  
Software Failures ◽  
Software Failure ◽  
Software Reliability Model ◽  
Operating Environments

The non-homogeneous Poisson process (NHPP) software has a crucial role in computer systems. Furthermore, the software is used in various environments. It was developed and tested in a controlled environment, while real-world operating environments may be different. Accordingly, the uncertainty of the operating environment must be considered. Moreover, predicting software failures is commonly an important part of study, not only for software developers, but also for companies and research institutes. Software reliability model can measure and predict the number of software failures, software failure intervals, software reliability, and failure rates. In this paper, we propose a new model with an inflection factor of the fault detection rate function, considering the uncertainty of operating environments and analyzing how the predicted value of the proposed new model is different than the other models. We compare the proposed model with several existing NHPP software reliability models using real software failure datasets based on ten criteria. The results show that the proposed new model has significantly better goodness-of-fit and predictability than the other models.

Case Study Investigation of the Fault Detection and Error Locating Effects of Architecture-based Software Testing

2020 ◽  
Vol 30 (02) ◽  
pp. 191-216
Author(s):  
Jihyun Lee ◽  
Sungwon Kang
Keyword(s):  
Fault Detection ◽  
Software Testing ◽  
Case Studies ◽  
Detection Rate ◽  
Software System ◽  
Contributing Factors ◽  
Types Of Faults ◽  
Test Architecture

For software testing, it is well known that the architecture of a software system can be utilized to enhance testability, fault detection and error locating. However, how much and what effects architecture-based software testing has on software testing have been rarely studied. Thus, this paper undertakes case study investigation of the effects of architecture-based software testing specifically with respect to fault detection and error locating. Through comparing the outcomes with the conventional testing approaches that are not based on test architectures, we confirm the effectiveness of architecture-based software testing with respect to fault detection and error locating. The case studies show that using test architecture can improve fault detection rate by 44.1%–88.5% and reduce error locating time by 3%–65.2%, compared to the conventional testing that does not rely on test architecture. With regard to error locating, the scope of relevant components or statements was narrowed by leveraging test architecture for approximately 77% of the detected faults. We also show that architecture-based testing could provide a means of defining an exact oracle or oracles with range values. This study shows by way of case studies the extent to which architecture-based software testing can facilitate detecting certain types of faults and locating the errors that cause such faults. In addition, we discuss the contributing factors of architecture-based software testing which enable such enhancement in fault detection and error locating.

A Novel Markov Model and its Application to BIT System

2012 ◽  
Vol 239-240 ◽  
pp. 721-725
Author(s):  
Wen Rong Zheng ◽  
Shu Zong Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Markov Model ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Intermittent Fault ◽  
System A ◽  
Main Factor

Intermittent fault is the main factor for the raise of false alarm during the process of the detection in built-in test (BIT). Two-state Markov model and three-state Markov model for test is built for system fault diagnosis with BIT. According to the application of BIT in some complex system, a comparison of the false alarm rate between two-state Markov model and three-state Markov model is present, which shows we can reduce the false alarm rate (FAR) and improve fault detection rate by using three-state Markov model in BIT.

GENERAL FRAMEWORK FOR CHANGE POINT PROBLEM IN SOFTWARE RELIABILITY AND RELATED RELEASE TIME PROBLEM

2009 ◽  
Vol 16 (06) ◽  
pp. 567-579 ◽  
Author(s):  
P. K. KAPUR ◽  
R. B. GARG ◽  
ANU. G. AGGARWAL ◽  
ABHISHEK TANDON
Keyword(s):  
Fault Detection ◽  
Change Point ◽  
Detection Rate ◽  
Goodness Of Fit ◽  
Release Time ◽  
Point Problem ◽  
Modeling Framework ◽  
Change Point Problem ◽  
Time Problem ◽  
Release Time Problem

Reliable software is the need of the hour especially as it is an indispensable part of our new technological world. Many SRGMs have been proposed considering the change point approach in literature. Change point is defined as the point where the fault detection rate changes, it happens due to number of reasons viz, proficiency of the testing team, nature of faults to be detected etc. In this paper we develop a generalized modeling framework incorporating change point discussing the changing nature of fault detection rate and form a related release time problem which minimizes the total cost of the software and maintains a desirable level of reliability. A numerical example is given for the release problem and the proposed models are validated on real software error data to show their goodness of fit and applicability.

Modeling Two-Dimensional Framework for Multi-Upgradations of a Software with Change Point

2016 ◽  
Vol 23 (06) ◽  
pp. 1640008 ◽  
Author(s):  
Anshul Tickoo ◽  
Ajit K. Verma ◽  
Sunil K. Khatri ◽  
P. K. Kapur
Keyword(s):  
Fault Detection ◽  
Change Point ◽  
Detection Rate ◽  
Real Data ◽  
Testing Time ◽  
Two Dimensional ◽  
Data Set ◽  
Software Companies ◽  
Business Efficiency ◽  
Change Point Models

Across the globe almost every organization is dependent on information technology for increasing their business efficiency. This has led to huge demand for reliable and good quality software. Innovation is most important to attain success in software industry. Software companies need to keep bringing upgradations or add-ons in the software to compete in the market. In the present framework we propose a generalized mathematical modeling for multiple software releases. In this framework we examine the collective effect of testing time and resources using Cobb–Douglas production function for defining the failure phenomenon using a software reliability growth model (SRGM). In this paper, we consider the practical scenario where there is a possibility of change in the fault detection rate. Fault detection rate can be affected by various factors like testing environment, testing strategy and allocation of resources. Change in these factors during testing phase can lead to increase or decrease in failure intensity function. The time point at which abrupt fluctuations in fault detection rate take place is known as change point. In this paper, a generalized framework for developing a two-dimensional SRGM with change point for multiple software releases has been discussed. We have derived various existing change point models using the proposed framework. The developed models have been validated on real data set.

scholarly journals Efficiency Enhancement in Regression Test Case Prioritization Technique

2019 ◽  
Vol 8 (12) ◽  
pp. 5445-5451
Keyword(s):  
Fault Detection ◽  
Software Quality ◽  
Detection Rate ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Efficiency Enhancement ◽  
Average Percentage ◽  
Regression Test ◽  
Expensive Process

Regression testing is an important, but expensive, process that has a powerful impact on software quality. Unfortunately all the test cases, existing and newly added, cannot be re-executed due to insufficient resources. In this scenario, prioritization of test case helps in improving the efficacy of regression testing by arranging the test cases in such a way that the most beneficial (that has the potential to detect the more number of faults) are executed first. Previous work and existing prioritization techniques, though detects faults, but there is a need of improved techniques to enhance the process of regression testing by improving the fault detection rate. The new technique, proposed in this paper, gives improved result than the existing ones. The comparison of the effectiveness of the proposed approach is done with other prioritization and non-prioritization orderings. The result of the proposed approach shows higher average percentage of faults detected (APFD) values. Also, the performance is evaluated and it is observed that the capability of the proposed method outperforms other algorithms by enhancing the fault detection rate.

Open Source Software Reliability Model with the Decreasing Trend of Fault Detection Rate

2018 ◽  
Vol 62 (9) ◽  
pp. 1301-1312
Author(s):  
Jinyong Wang ◽  
Xiaoping Mi
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Real World ◽  
Detection Rate ◽  
Failure Process ◽  
Model Performance ◽  
Predictive Performance ◽  
Proposed Model

Abstract Software reliability assessment methods have been changed from closed to open source software (OSS). Although numerous new approaches for improving OSS reliability are formulated, they are not used in practice due to their inaccuracy. A new proposed model considering the decreasing trend of fault detection rate is developed in this study to effectively improve OSS reliability. We analyse the changes of the instantaneous fault detection rate over time by using real-world software fault count data from two actual OSS projects, namely, Apache and GNOME, to validate the proposed model performance. Results show that the proposed model with the decreasing trend of fault detection rate has better fitting and predictive performance than the traditional closed source software and other OSS reliability models. The proposed model for OSS can further accurately fit and predict the failure process and thus can assist in improving the quality of OSS systems in real-world OSS projects.

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