The Experts below are selected from a list of 1167 Experts worldwide ranked by ideXlab platform
Joydip Dhar - One of the best experts on this subject based on the ideXlab platform.
-
Reliability analysis of open source software systems considering the effect of previously released version
International Journal of Computers and Applications, 2018Co-Authors: Bhoopendra Pachauri, Ajay Kumar, Joydip DharAbstract:In this study, reliability analysis of open source software in an imperfect Debugging Environment has been discussed by considering the effect of detection rate and the remaining faults in successi...
-
Incorporating inflection S-shaped fault reduction factor to enhance software reliability growth
Applied Mathematical Modelling, 2015Co-Authors: Bhoopendra Pachauri, Joydip Dhar, Ajay KumarAbstract:Abstract Fault reduction factors (FRFs) play an important role in software reliability and are generally defined as the ratio of the total number of reduced faults to the total number of failures experienced. The behavior of FRFs is not fixed and can be affected by many factors, e.g., imperfect Debugging, resource allocations, and Debugging time lag. In most studies, either constant, increasing or decreasing FRFs have been considered. These are not sufficient to represent the realistic behavior of FRFs. We present three models in this study. In the first two models, an inflection S-shaped curve is considered as the FRF, and in the third model the FRF is constant. The first model functions in a perfect Debugging Environment whereas the second and third models function in an imperfect Debugging Environment. Here, the first two models are developed for single release software systems and the third is developed for multi-release software systems. Finally, a comparison is made with existing models in literature.
-
Software reliability growth model with logistic-exponential TEF in imperfect Debugging Environment
International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), 2014Co-Authors: Joydip Dhar, Seema Ingle, Yaminee SheshkerAbstract:The most important component of software quality is software reliability. Every software industry wants to develop error and fault free software. Software is systematically checked and detected faults are removed before it delivered into the market. Software reliability growth models help the software industries to develop fault free and reliable software. In this project, we have done an analysis of NHPP software reliability growth model based on the logistic-exponential testing-effort in imperfect Debugging Environment and also software release-time for reaching different reliability level is calculated. Calculations are done on the actual dataset which is study by Ohba 1984. Parameters are estimated using LSE in Matlab platform, some measures are calculated for evaluation of proposed model and compare our model with few existing model and observed that our proposed model is better fitted compare to other existing models.
-
modeling optimal release policy under fuzzy paradigm in imperfect Debugging Environment
Information & Software Technology, 2013Co-Authors: Bhoopendra Pachauri, Ajay Kumar, Joydip DharAbstract:Context: In this study, a software optimal release time with cost-reliability criteria has been discussed in an imperfect Debugging Environment. Objective: The motive of this study is to model uncertainty involved in estimated parameters of the software reliability growth model (SRGM). Method: Initially the reliability parameters of SRGM are estimated using least square estimation (LSE). Considering the uncertainty involved in the estimated parameters due to human behavior being subjective in nature and the dynamism of the testing Environment, the concept of fuzzy set theory is applicable in developing SRGM. Finally, using arithmetic operations on fuzzy numbers, the reliability and total software cost are calculated. Results: Various reliability measures have been computed at different levels of uncertainties, and a comparison has been made with the existing results reported in the literature. Conclusion: It is evident from the results that a better prediction of reliability measures, namely, software reliability and total software cost can be made under the fuzzy paradigm.
Shigeru Yamada - One of the best experts on this subject based on the ideXlab platform.
-
Software reliability modeling with imperfect Debugging and change of test Environment
2017 6th International Conference on Reliability Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2017Co-Authors: Shinji Inoue, Shigeru YamadaAbstract:We discuss Markovian software reliability modeling with imperfect Debugging Environment and the effect of change-point for considering more practical situation of a software reliability growth process. Further, we discuss a parameter estimation method for applying our model to observed data. Finally, we show numerical examples of our model, and check the performance of our model with an existing Markovian imperfect Debugging model by using actual software failure-occurrence time data.
-
imperfect Debugging models with two kinds of software hazard rate and their bayesian formulation
Electronics and Communications in Japan Part Iii-fundamental Electronic Science, 2001Co-Authors: Shigeru Yamada, Kouichi SeraAbstract:Ensuring the reliability of systems and their backbone, the software system has become increasingly important with the development of advanced information technology. Most of the models previously proposed as software evaluation techniques described the phenomena generating software failures or the events detecting faults. However, most of these models assumed a perfect Debugging Environment in which all of the faults detected during the testing phase or the operation stage are corrected or removed without introducing new faults. In practice, however, assuming an imperfect Debugging Environment which allows new faults to be introduced during correction is believed to be more realistic. Therefore, in this paper, we consider two kinds of software failures caused by faults introduced before testing began and new faults introduced during corrections to develop a software reliability model in an imperfect Debugging Environment and to examine the goodness-of-fit and validity of the model. We also introduce a Bayes theory for the constructed model and examine its applicability and utility to real data. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 84(3): 12–20, 2001
-
software reliability measurement in imperfect Debugging Environment and its application
Reliability Engineering & System Safety, 1993Co-Authors: Shigeru Yamada, Koichi Tokuno, Shunji OsakiAbstract:Abstract In practice, Debugging operations during the testing phase of software development are not always performed perfectly. In other words, not all the software faults detected are corrected and removed. Generally, this is called imperfect Debugging. In this paper, we discuss a software reliability growth model considering imperfect Debugging. Defining a random variable representing the cumulative number of faults corrected up to a specified testing time, this model is described by a semi-Markov process. Then, several quantitative measures are derived for software reliability assessment in an imperfect Debugging Environment. The application of this model to optimal software release problems is also discussed. Finally, numerical illustrations for software reliability measurement and optimal software release policies are presented.
Shunji Osaki - One of the best experts on this subject based on the ideXlab platform.
-
software reliability measurement in imperfect Debugging Environment and its application
Reliability Engineering & System Safety, 1993Co-Authors: Shigeru Yamada, Koichi Tokuno, Shunji OsakiAbstract:Abstract In practice, Debugging operations during the testing phase of software development are not always performed perfectly. In other words, not all the software faults detected are corrected and removed. Generally, this is called imperfect Debugging. In this paper, we discuss a software reliability growth model considering imperfect Debugging. Defining a random variable representing the cumulative number of faults corrected up to a specified testing time, this model is described by a semi-Markov process. Then, several quantitative measures are derived for software reliability assessment in an imperfect Debugging Environment. The application of this model to optimal software release problems is also discussed. Finally, numerical illustrations for software reliability measurement and optimal software release policies are presented.
Kisok Kong - One of the best experts on this subject based on the ideXlab platform.
-
LCTES - A Design and Implementation of a Remote Debugging Environment for Embedded Internet Software
Lecture Notes in Computer Science, 2001Co-Authors: Kisok KongAbstract:It is necessary to use development tools in developing embedded real-time application software for Internet appliances. In this paper, we describe an integrated remote Debugging Environment for Q+(QPlus) real-time kernel which has been built for an embedded internet application. The remote development toolset called Q+Esto consists of several independent support tools: an interactive shell, a remote debugger, a resource monitor, a target manager and a debug agent. Using the remote debugger on the host, the developer can spawn and debug tasks on the target run-time system. It can also be attached to already-running tasks spawned from the application or from interactive shell. Applicatoin code can be viewed as C source, or as assembly-level code. It incorporates a variety of display windows for source, registers, local/global variables, stack frame, memory, event traces and so on. The target manager implements common functions that are shared by Esto tools, e.g., the host target communiction, object file loading, and management of target-resident host tool's memory pool and of target system symbol-table, and so on. These functions are called OPEN C APIs and they greatly improve the extensibility of the Esto Toolset. Debug agent is a daemon task on real-time operating systems in the target system. It gets requests from the host tools including debugger, interprets the requests, executes them and sends the results to the host.
-
a design and implementation of a remote Debugging Environment for embedded internet software
Lecture Notes in Computer Science, 2001Co-Authors: Kisok KongAbstract:It is necessary to use development tools in developing embedded real-time application software for Internet appliances. In this paper, we describe an integrated remote Debugging Environment for Q+(QPlus) real-time kernel which has been built for an embedded internet application. The remote development toolset called Q+Esto consists of several independent support tools: an interactive shell, a remote debugger, a resource monitor, a target manager and a debug agent. Using the remote debugger on the host, the developer can spawn and debug tasks on the target run-time system. It can also be attached to already-running tasks spawned from the application or from interactive shell. Applicatoin code can be viewed as C source, or as assembly-level code. It incorporates a variety of display windows for source, registers, local/global variables, stack frame, memory, event traces and so on. The target manager implements common functions that are shared by Esto tools, e.g., the host target communiction, object file loading, and management of target-resident host tool's memory pool and of target system symbol-table, and so on. These functions are called OPEN C APIs and they greatly improve the extensibility of the Esto Toolset. Debug agent is a daemon task on real-time operating systems in the target system. It gets requests from the host tools including debugger, interprets the requests, executes them and sends the results to the host.
Bhoopendra Pachauri - One of the best experts on this subject based on the ideXlab platform.
-
Reliability analysis of open source software systems considering the effect of previously released version
International Journal of Computers and Applications, 2018Co-Authors: Bhoopendra Pachauri, Ajay Kumar, Joydip DharAbstract:In this study, reliability analysis of open source software in an imperfect Debugging Environment has been discussed by considering the effect of detection rate and the remaining faults in successi...
-
Incorporating inflection S-shaped fault reduction factor to enhance software reliability growth
Applied Mathematical Modelling, 2015Co-Authors: Bhoopendra Pachauri, Joydip Dhar, Ajay KumarAbstract:Abstract Fault reduction factors (FRFs) play an important role in software reliability and are generally defined as the ratio of the total number of reduced faults to the total number of failures experienced. The behavior of FRFs is not fixed and can be affected by many factors, e.g., imperfect Debugging, resource allocations, and Debugging time lag. In most studies, either constant, increasing or decreasing FRFs have been considered. These are not sufficient to represent the realistic behavior of FRFs. We present three models in this study. In the first two models, an inflection S-shaped curve is considered as the FRF, and in the third model the FRF is constant. The first model functions in a perfect Debugging Environment whereas the second and third models function in an imperfect Debugging Environment. Here, the first two models are developed for single release software systems and the third is developed for multi-release software systems. Finally, a comparison is made with existing models in literature.
-
modeling optimal release policy under fuzzy paradigm in imperfect Debugging Environment
Information & Software Technology, 2013Co-Authors: Bhoopendra Pachauri, Ajay Kumar, Joydip DharAbstract:Context: In this study, a software optimal release time with cost-reliability criteria has been discussed in an imperfect Debugging Environment. Objective: The motive of this study is to model uncertainty involved in estimated parameters of the software reliability growth model (SRGM). Method: Initially the reliability parameters of SRGM are estimated using least square estimation (LSE). Considering the uncertainty involved in the estimated parameters due to human behavior being subjective in nature and the dynamism of the testing Environment, the concept of fuzzy set theory is applicable in developing SRGM. Finally, using arithmetic operations on fuzzy numbers, the reliability and total software cost are calculated. Results: Various reliability measures have been computed at different levels of uncertainties, and a comparison has been made with the existing results reported in the literature. Conclusion: It is evident from the results that a better prediction of reliability measures, namely, software reliability and total software cost can be made under the fuzzy paradigm.
