In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon the reliable / unreliable of the developed software very quickly. The procedure adopted for this is, Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters are estimated using Maximum Likelihood Estimation.
Software Process Control on Ungrouped Data: Log-Power ModelWaqas Tariq
Statistical Process Control (SPC) is the best choice to monitor software reliability process. It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of failures which is ungrouped data using an infinite failure mean value function of Log-Power model, which is Non-Homogenous Poisson Process (NHPP) based. The Maximum Likelihood Estimation (MLE) approach is used to estimate the unknown parameters of the model.
ESTIMATING HANDLING TIME OF SOFTWARE DEFECTScsandit
The problem of accurately predicting handling time for software defects is of great practical
importance. However, it is difficult to suggest a practical generic algorithm for such estimates,
due in part to the limited information available when opening a defect and the lack of a uniform
standard for defect structure. We suggest an algorithm to address these challenges that is
implementable over different defect management tools. Our algorithm uses machine learning
regression techniques to predict the handling time of defects based on past behaviour of similar
defects. The algorithm relies only on a minimal set of assumptions about the structure of the
input data. We show how an implementation of this algorithm predicts defect handling time with
promising accuracy results
Pareto Type II Based Software Reliability Growth ModelWaqas Tariq
The past 4 decades have seen the formulation of several software reliability growth models to predict the reliability and error content of software systems. This paper presents Pareto type II model as a software reliability growth model, together with expressions for various reliability performance measures. Theory of probability, distribution function, probability distributions plays major role in software reliability model building. This paper presents estimation procedures to access reliability of a software system using Pareto distribution, which is based on Non Homogenous Poisson Process (NHPP).
COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION...ijccmsjournal
Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An
important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors
increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..
Software Process Control on Ungrouped Data: Log-Power ModelWaqas Tariq
Statistical Process Control (SPC) is the best choice to monitor software reliability process. It assists the software development team to identify and actions to be taken during software failure process and hence, assures better software reliability. In this paper we propose a control mechanism based on the cumulative observations of failures which is ungrouped data using an infinite failure mean value function of Log-Power model, which is Non-Homogenous Poisson Process (NHPP) based. The Maximum Likelihood Estimation (MLE) approach is used to estimate the unknown parameters of the model.
ESTIMATING HANDLING TIME OF SOFTWARE DEFECTScsandit
The problem of accurately predicting handling time for software defects is of great practical
importance. However, it is difficult to suggest a practical generic algorithm for such estimates,
due in part to the limited information available when opening a defect and the lack of a uniform
standard for defect structure. We suggest an algorithm to address these challenges that is
implementable over different defect management tools. Our algorithm uses machine learning
regression techniques to predict the handling time of defects based on past behaviour of similar
defects. The algorithm relies only on a minimal set of assumptions about the structure of the
input data. We show how an implementation of this algorithm predicts defect handling time with
promising accuracy results
Pareto Type II Based Software Reliability Growth ModelWaqas Tariq
The past 4 decades have seen the formulation of several software reliability growth models to predict the reliability and error content of software systems. This paper presents Pareto type II model as a software reliability growth model, together with expressions for various reliability performance measures. Theory of probability, distribution function, probability distributions plays major role in software reliability model building. This paper presents estimation procedures to access reliability of a software system using Pareto distribution, which is based on Non Homogenous Poisson Process (NHPP).
COMPUTATIONAL COMPLEXITY COMPARISON OF MULTI-SENSOR SINGLE TARGET DATA FUSION...ijccmsjournal
Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An
important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors
increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..
A report on designing a model for improving CPU Scheduling by using Machine L...MuskanRath1
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
There are several methods to predict the length of the CPU bursts, such as the exponential averaging method, however, these methods may not give accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based on the best approach to estimate the length of the CPU bursts for processes. We will make use of Bayesian Theory for our model as a classifier tool that will decide which process will execute first in the ready queue. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. Furthermore, applying attribute selection techniques improves the performance in terms of space, time, and estimation.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
USING CUCKOO ALGORITHM FOR ESTIMATING TWO GLSD PARAMETERS AND COMPARING IT WI...ijcsit
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
Fault localization is time-consuming and difficult,
which makes it the bottleneck of the
debugging progress. To help facilitate this task, t
here exist many fault localization techniques
that help narrow down the region of the suspicious
code in a program. Better accuracy in fault
localization is achieved from heavy computation cos
t. Fault localization techniques that can
effectively locate faults also manifest slow respon
se rate. In this paper, we promote the use of
pre-computing to distribute the time-intensive comp
utations to the idle period of coding phase,
in order to speed up such techniques and achieve bo
th low-cost and high accuracy. We raise the
research problems of finding suitable techniques th
at can be pre-computed and adapt it to the
pre-computing paradigm in a continuous integration
environment. Further, we use an existing
fault localization technique to demonstrate our res
earch exploration, and shows visions and
challenges of the related methodologies.
IMPLEMENTATION OF COMPACTION ALGORITHM FOR ATPG GENERATED PARTIALLY SPECIFIED...VLSICS Design
In this paper the ATPG is implemented using C++. This ATPG is based on fault equivalence concept in which the number of faults gets reduced before compaction method. This ATPG uses the line justification and error propagation to find the test vectors for reduced fault set with the aid of controllability and observability. Single stuck at fault model is considered. The programs are developed for fault equivalence method, controllability Observability, automatic test pattern generation and test data compaction using object oriented language C++. ISCAS 85 C17 circuit was used for analysis purpose along with other circuits. Standard ISCAS (International Symposium on Circuits And Systems) netlist format was used. The flow charts and results for ISCAS 85 C17 circuits along with other netlists are given in this paper. The test vectors generated by the ATPG further compacted to reduce the test vector data. The algorithm is developed for the test vector compaction and discussed along with results.
REDUCING THE COGNITIVE LOAD ON ANALYSTS THROUGH HAMMING DISTANCE BASED ALERT ...IJNSA Journal
Previous work introduced the idea of grouping alerts at a Hamming distance of 1 to achieve lossless alert aggregation; such aggregated meta-alerts were shown to increase alert interpretability. However, a mean
of 84023 daily Snort alerts were reduced to a still formidable 14099 meta-alerts. In this work, we address
this limitation by investigating several approaches that all contribute towards reducing the burden on the
analyst and providing timely analysis. We explore minimizing the number of both alerts and data elements
by aggregating at Hamming distances greater than 1. We show how increasing bin sizes can improve
aggregation rates. And we provide a new aggregation algorithm that operates up to an order of magnitude
faster at Hamming distance 1. Lastly, we demonstrate the broad applicability of this approach through
empirical analysis of Windows security alerts, Snort alerts, netflow records, and DNS logs. The result is a
reduction in the cognitive load on analysts by minimizing the overall number of alerts and the number of
data elements that need to be reviewed in order for an analyst to evaluate the set of original alerts.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
Assessing Software Reliability Using SPC – An Order Statistics Approach IJCSEA Journal
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or Non-Homogeneous Poisson Processes (NHPP), with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. Order statistics are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many books. Statistical Process Control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper we proposed a control mechanism based on order statistics of cumulative quantity between observations of time domain
failure data using mean value function of Half Logistics Distribution (HLD) based on NHPP.
Assessing Software Reliability Using SPC – An Order Statistics ApproachIJCSEA Journal
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or Non-Homogeneous Poisson Processes (NHPP), with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. Order statistics are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many books. Statistical Process Control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper we proposed a control mechanism based on order statistics of cumulative quantity between observations of time domain
failure data using mean value function of Half Logistics Distribution (HLD) based on NHPP.
SRGM with Imperfect Debugging by Genetic Algorithmsijseajournal
Computer software has progressively turned out to be an
essential component in modern technologies. Penalty costs resulting from
software failures are often more considerable than software developing costs.
Debugging decreases the error content but expands the software development
costs. To improve the software quality, software reliability engineering plays
an important role in many aspects throughout the software life cycle. In this
paper, we incorporate both imperfect debugging and change-point problem into
the software reliability growth model(SRGM) based on the well-known
exponential distribution the parameter estimation is studied and the proposed
model is compared with the some existing models in the literature and is find to
be better.
Identification of Outliersin Time Series Data via Simulation Studyiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A report on designing a model for improving CPU Scheduling by using Machine L...MuskanRath1
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
There are several methods to predict the length of the CPU bursts, such as the exponential averaging method, however, these methods may not give accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based on the best approach to estimate the length of the CPU bursts for processes. We will make use of Bayesian Theory for our model as a classifier tool that will decide which process will execute first in the ready queue. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. Furthermore, applying attribute selection techniques improves the performance in terms of space, time, and estimation.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
USING CUCKOO ALGORITHM FOR ESTIMATING TWO GLSD PARAMETERS AND COMPARING IT WI...ijcsit
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
Fault localization is time-consuming and difficult,
which makes it the bottleneck of the
debugging progress. To help facilitate this task, t
here exist many fault localization techniques
that help narrow down the region of the suspicious
code in a program. Better accuracy in fault
localization is achieved from heavy computation cos
t. Fault localization techniques that can
effectively locate faults also manifest slow respon
se rate. In this paper, we promote the use of
pre-computing to distribute the time-intensive comp
utations to the idle period of coding phase,
in order to speed up such techniques and achieve bo
th low-cost and high accuracy. We raise the
research problems of finding suitable techniques th
at can be pre-computed and adapt it to the
pre-computing paradigm in a continuous integration
environment. Further, we use an existing
fault localization technique to demonstrate our res
earch exploration, and shows visions and
challenges of the related methodologies.
IMPLEMENTATION OF COMPACTION ALGORITHM FOR ATPG GENERATED PARTIALLY SPECIFIED...VLSICS Design
In this paper the ATPG is implemented using C++. This ATPG is based on fault equivalence concept in which the number of faults gets reduced before compaction method. This ATPG uses the line justification and error propagation to find the test vectors for reduced fault set with the aid of controllability and observability. Single stuck at fault model is considered. The programs are developed for fault equivalence method, controllability Observability, automatic test pattern generation and test data compaction using object oriented language C++. ISCAS 85 C17 circuit was used for analysis purpose along with other circuits. Standard ISCAS (International Symposium on Circuits And Systems) netlist format was used. The flow charts and results for ISCAS 85 C17 circuits along with other netlists are given in this paper. The test vectors generated by the ATPG further compacted to reduce the test vector data. The algorithm is developed for the test vector compaction and discussed along with results.
REDUCING THE COGNITIVE LOAD ON ANALYSTS THROUGH HAMMING DISTANCE BASED ALERT ...IJNSA Journal
Previous work introduced the idea of grouping alerts at a Hamming distance of 1 to achieve lossless alert aggregation; such aggregated meta-alerts were shown to increase alert interpretability. However, a mean
of 84023 daily Snort alerts were reduced to a still formidable 14099 meta-alerts. In this work, we address
this limitation by investigating several approaches that all contribute towards reducing the burden on the
analyst and providing timely analysis. We explore minimizing the number of both alerts and data elements
by aggregating at Hamming distances greater than 1. We show how increasing bin sizes can improve
aggregation rates. And we provide a new aggregation algorithm that operates up to an order of magnitude
faster at Hamming distance 1. Lastly, we demonstrate the broad applicability of this approach through
empirical analysis of Windows security alerts, Snort alerts, netflow records, and DNS logs. The result is a
reduction in the cognitive load on analysts by minimizing the overall number of alerts and the number of
data elements that need to be reviewed in order for an analyst to evaluate the set of original alerts.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
Assessing Software Reliability Using SPC – An Order Statistics Approach IJCSEA Journal
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or Non-Homogeneous Poisson Processes (NHPP), with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. Order statistics are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many books. Statistical Process Control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper we proposed a control mechanism based on order statistics of cumulative quantity between observations of time domain
failure data using mean value function of Half Logistics Distribution (HLD) based on NHPP.
Assessing Software Reliability Using SPC – An Order Statistics ApproachIJCSEA Journal
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or Non-Homogeneous Poisson Processes (NHPP), with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. Order statistics are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many books. Statistical Process Control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper we proposed a control mechanism based on order statistics of cumulative quantity between observations of time domain
failure data using mean value function of Half Logistics Distribution (HLD) based on NHPP.
SRGM with Imperfect Debugging by Genetic Algorithmsijseajournal
Computer software has progressively turned out to be an
essential component in modern technologies. Penalty costs resulting from
software failures are often more considerable than software developing costs.
Debugging decreases the error content but expands the software development
costs. To improve the software quality, software reliability engineering plays
an important role in many aspects throughout the software life cycle. In this
paper, we incorporate both imperfect debugging and change-point problem into
the software reliability growth model(SRGM) based on the well-known
exponential distribution the parameter estimation is studied and the proposed
model is compared with the some existing models in the literature and is find to
be better.
Identification of Outliersin Time Series Data via Simulation Studyiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
BPSO&1-NN algorithm-based variable selection for power system stability ident...IJAEMSJORNAL
Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
Testing the performance of the power law process model considering the use of...IJCSEA Journal
Within the class of non-homogeneous Poisson process (NHPP) models and as a result of the simplicity of
the mathematical computations of the Power Law Process (PLP) model and the attractive physical
explanation of its parameters, this model has found considerable attention in repairable systems literature.
In this article, we conduct the investigation of new estimation approach, the regression estimation
procedure, on the performance of the parametric PLP model. The regression approach for estimating the
unknown parameters of the PLP model through the mean time between failure (TBF) function is evaluated
against the maximum likelihood estimation (MLE) approach. The results from the regression and MLE
approaches are compared based on three error evaluation criteria in terms of parameter estimation and its
precision, the numerical application shows the effectiveness of the regression estimation approach at
enhancing the predictive accuracy of the TBF measure.
Code coverage based test case selection and prioritizationijseajournal
Regression Testing is exclusively executed to guarantee the desirable functionality of existing software
after pursuing quite a few amendments or variations in it. Perhaps, it testifies the quality of the modified
software by concealing the regressions or
software bugs in both functional and non
-
functional applications
of the system. In fact, the maintenance of test suite is enormous as it necessitates a big investment of time
and money on test cases on a large scale. So, minimizing the test suite becomes
the indispensable requisite
to lessen the budget on regression testing. Precisely, this research paper aspires to present an innovative
approach for the effective
selection and prioritization of test cases which in return may procure a maximum
code average
Time alignment techniques for experimental sensor dataIJCSES Journal
Experimental data is subject to data loss, which presents a challenge for representing the data with a
proper time scale. Additionally, data from separate measurement systems need to be aligned in order to
use the data cooperatively. Due to the need for accurate time alignment, various practical techniques are
presented along with an illustrative example detailing each step of the time alignment procedure for actual
experimental data from an Unmanned Aerial Vehicle (UAV). Some example MATLAB code is also
provided.
BIO-INSPIRED MODELLING OF SOFTWARE VERIFICATION BY MODIFIED MORAN PROCESSESIJCSEA Journal
A new approach for the control and prediction of verification activities for large safety-relevant software
systems will be presented in this paper. The model is applied on a macroscopic system level and based on
so-called Moran processes, which originate from mathematical biology and allow for the description
ofphenomena as, for instance, genetic drift. Beside the theoretical foundations of this novel approach, its
application on a real-world example from the medical engineering domain will be discussed.
Bio-Inspired Modelling of Software Verification by Modified Moran ProcessesIJCSEA Journal
A new approach for the control and prediction of verification activities for large safety-relevant software systems will be presented in this paper. The model is applied on a macroscopic system level and based on so-called Moran processes, which originate from mathematical biology and allow for the description of phenomena as, for instance, genetic drift. Beside the theoretical foundations of this novel approach, its application on a real-world example from the medical engineering domain will be discussed.
A comparison of three chromatographic retention time prediction modelsAndrew McEachran
High resolution mass spectrometry (HRMS) data has revolutionized the identification of environmental contaminants through non-targeted analyses (NTA). However, data processing and chemical identification and prioritization remain challenging due to the vast number of unknowns observed in NTA. The ideal NTA workflow requires harmonized data and tools from a variety of sources to allow the most probable and confirmed identifications. One such tool is the use of chromatographic retention time (RT). Comparing predicted RT of candidate structures to observed RT allows for additional specificity towards ultimate identification. In this work, three RT prediction models were evaluated on the same set of chemicals: 1) a logP-based model, 2) a model generated in ACD/ChromGenius, and 3) a Quantitative Structure Retention Relationship model, OPERA-RT. Our results indicate that both ACD/ChromGenius and OPERA-RT outperform the logP-based model. Between the two, OPERA-RT produced a slightly better fit on the entire set of structures than ACD/ChromGenius (R2 values of 0.85 to 0.83). Further, OPERA-RT, generated within the US EPA’s National Center for Computational Toxicology, predicted 96% of RTs within a 15% (+/-) chromatographic time window of experimental RTs. Finally, to test an NTA workflow, candidate structures were generated for formulae in the test set using the US EPA’s CompTox Chemistry Dashboard and RTs for all candidates were predicted using both ACD/ChromGenius and OPERA-RT. RT screening windows were applied to screen out unlikely candidate chemicals and enhance potential identification. Compared to ACD/ChromGenius, OPERA-RT screened out a greater percentage of the candidate structures by RT, but retained fewer of the known chemicals. This research demonstrates the potential value of including RT prediction in NTA workflows and indicates the potential value of OPERA-RT predictions to support our NTA investigations. This abstract does not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
BIO-INSPIRED MODELLING OF SOFTWARE VERIFICATION BY MODIFIED MORAN PROCESSESIJCSEA Journal
A new approach for the control and prediction of verification activities for large safety-relevant software
systems will be presented in this paper. The model is applied on a macroscopic system level and based on
so-called Moran processes, which originate from mathematical biology and allow for the description
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DETECTION OF RELIABLE SOFTWARE USING SPRT ON TIME DOMAIN DATA
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
DOI : 10.5121/ijcsea.2011.1407 73
DETECTION OF RELIABLE SOFTWARE
USING SPRT ON TIME DOMAIN DATA
G.Krishna Mohan1
and Dr. Satya Prasad Ravi2
1
Reader, Dept. of Computer Science, P.B.Siddhartha college
Vijayawada, Andhrapradesh, India.
km_mm_2000@yahoo.com
2
Associate Professor, Dept. of Computer Science & Engg., Acharya Nagrjuna University,
Nagarjuna Nagar, Guntur, Andhrapradesh, India
profrsp@gmail.com
ABSTRACT
In Classical Hypothesis testing volumes of data is to be collected and then the conclusions are drawn which
may take more time. But, Sequential Analysis of statistical science could be adopted in order to decide upon
the reliable / unreliable of the developed software very quickly. The procedure adopted for this is,
Sequential Probability Ratio Test (SPRT). In the present paper we proposed the performance of SPRT on
Time domain data using Weibull model and analyzed the results by applying on 5 data sets. The parameters
are estimated using Maximum Likelihood Estimation.
KEYWORDS
Weibull model, Sequential Probability Ratio Test, Maximum Likelihood Estimation,
Decision lines, Software Reliability, Time domain data.
1. INTRODUCTION
Wald's procedure is particularly relevant if the data is collected sequentially. Sequential Analysis
is different from Classical Hypothesis Testing were the number of cases tested or collected is
fixed at the beginning of the experiment. In Classical Hypothesis Testing the data collection is
executed without analysis and consideration of the data. After all data is collected the analysis is
done and conclusions are drawn. However, in Sequential Analysis every case is analysed directly
after being collected, the data collected upto that moment is then compared with certain threshold
values, incorporating the new information obtained from the freshly collected case. This approach
allows one to draw conclusions during the data collection, and a final conclusion can possibly be
reached at a much earlier stage as is the case in Classical Hypothesis Testing. The advantages of
Sequential Analysis are easy to see. As data collection can be terminated after fewer cases and
decisions taken earlier, the savings in terms of human life and misery, and financial savings,
might be considerable.
In the analysis of software failure data we often deal with either Time Between Failures or failure
count in a given time interval. If it is further assumed that the average number of recorded failures
in a given time interval is directly proportional to the length of the interval and the random
number of failure occurrences in the interval is explained by a Poisson process then we know that
the probability equation of the stochastic process representing the failure occurrences is given by
a homogeneous poisson process with the expression
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
74
( )
( )
!
nt
e t
P N t n
n
λ
λ−
= = (1.1)
Stieber[5] observes that if classical testing strategies are used, the application of software
reliability growth models may be difficult and reliability predictions can be misleading. However,
he observes that statistical methods can be successfully applied to the failure data. He
demonstrated his observation by applying the well-known sequential probability ratio test of
Wald [4] for a software failure data to detect unreliable software components and compare the
reliability of different software versions. In this paper we consider popular SRGM Exponential
imperfect debugging model and adopt the principle of Stieber in detecting unreliable software
components in order to accept or reject the developed software. The theory proposed by Stieber is
presented in Section 2 for a ready reference. Extension of this theory to the SRGM – Weibull is
presented in Section 3. Maximum Likelihood parameter estimation method is presented in
Section 4. Application of the decision rule to detect unreliable software components with respect
to the proposed SRGM is given in Section 5.
2. WALD'S SEQUENTIAL TEST FOR A POISSON PROCESS
The sequential probability ratio test was developed by A.Wald at Columbia University in 1943.
Due to its usefulness in development work on military and naval equipment it was classified as
‘Restricted’ by the Espionage Act (Wald, 1947). A big advantage of sequential tests is that they
require fewer observations (time) on the average than fixed sample size tests. SPRTs are widely
used for statistical quality control in manufacturing processes. An SPRT for homogeneous
Poisson processes is described below.
Let {N(t),t ≥0} be a homogeneous Poisson process with rate ‘λ’. In our case, N(t) = number of
failures up to time ‘ t’ and ‘λ’ is the failure rate (failures per unit time ). Suppose that we put a
system on test (for example a software system, where testing is done according to a usage profile
and no faults are corrected) and that we want to estimate its failure rate ‘λ’. We can not expect to
estimate ‘λ’ precisely. But we want to reject the system with a high probability if our data
suggest that the failure rate is larger than λ1 and accept it with a high probability, if it’s smaller
than λ0. As always with statistical tests, there is some risk to get the wrong answers. So we have
to specify two (small) numbers ‘α’ and ‘β’, where ‘α’ is the probability of falsely rejecting the
system. That is rejecting the system even if λ ≤ λ0. This is the "producer’s" risk. β is the
probability of falsely accepting the system .That is accepting the system even if λ ≥ λ1. This is
the “consumer’s” risk. With specified choices of λ0 and λ1 such that 0 < λ0 < λ1, the probability
of finding N(t) failures in the time span (0,t ) with λ1, λ0 as the failure rates are respectively
given by
[ ] ( )1
1
1
( )!
N tt
e t
P
N t
λ
λ−
= (2.1)
[ ] ( )0
0
0
( )!
N tt
e t
P
N t
λ
λ−
= (2.2)
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
75
The ratio 1
0
P
P
at any time ’t’ is considered as a measure of deciding the truth towards 0λ or 1λ ,
given a sequence of time instants say 1 2 3 ........ Kt t t t< < < < and the corresponding
realizations 1 2( ), ( ),........ ( )KN t N t N t of N(t). Simplification of 1
0
P
P
gives
( )
1 1
0 1
0 0
exp( )
N t
P
t
P
λ
λ λ
λ
= − +
The decision rule of SPRT is to decide in favor of 1λ , in favor of 0λ or to continue by observing
the number of failures at a later time than 't' according as 1
0
P
P
is greater than or equal to a
constant say A, less than or equal to a constant say B or in between the constants A and B. That
is, we decide the given software product as unreliable, reliable or continue [3] the test process
with one more observation in failure data, according as
1
0
P
A
P
≥ (2.3)
1
0
P
B
P
≤ (2.4)
1
0
P
B A
P
< < (2.5)
The approximate values of the constants A and B are taken as
1
A
β
α
−
≅ ,
1
B
β
α
≅
−
Where ‘α ’ and ‘ β ’ are the risk probabilities as defined earlier. A simplified version of the
above decision processes is to reject the system as unreliable if N(t) falls for the first time above
the line ( ) 2.UN t a t b= + (2.6)
to accept the system to be reliable if N(t) falls for the first time below the line
( ) 1.LN t a t b= − (2.7)
To continue the test with one more observation on (t, N(t)) as the random graph of [t, N(t)]
is between the two linear boundaries given by equations (2.6) and (2.7) where
1 0
1
0
log
a
λ λ
λ
λ
−
=
(2.8)
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
76
1
1
0
1
log
log
b
α
β
λ
λ
−
=
(2.9)
2
1
0
1
log
log
b
β
α
λ
λ
−
=
(2.10)
The parameters ,α β , 0λ and 1λ can be chosen in several ways. One way suggested by Stieber is
( )
0
.log
1
q
q
λ
λ =
−
,
( )
1
.log
1
q
q
q
λ
λ =
−
1
0
where q
λ
λ
=
If λ0 and λ1 are chosen in this way, the slope of NU (t) and NL (t) equals λ. The other two ways of
choosing λ0 and λ1 are from past projects (for a comparison of the projects) and from part of the
data to compare the reliability of different functional areas (components).
3. SEQUENTIAL TEST FOR SOFTWARE RELIABILITY GROWTH MODELS
In Section 2, for the Poisson process we know that the expected value of N(t) = λt called the
average number of failures experienced in time 't' .This is also called the mean value function of
the Poisson process. On the other hand if we consider a Poisson process with a general function
(not necessarily linear) m(t) as its mean value function the probability equation of a such a
process is
[ ]
[ ] ( )( )
( ) . , 0,1,2,
!
y
m tm t
P N t Y e y
y
−
= = = − −− −
Depending on the forms of m(t) we get various Poisson processes called NHPP. For our Weibull
model the mean value function is given as ( ) ( )
( )
2
1
bt
m t a e
−
= − where 0, 0a b> >
We may write
[ ]1
( )( )
1
1
. ( )
( )!
N tm t
e m t
P
N t
−
=
[ ]0
( )( )
0
0
. ( )
( )!
N tm t
e m t
P
N t
−
=
Where, 1( )m t , 0 ( )m t are values of the mean value function at specified sets of its parameters
indicating reliable software and unreliable software respectively. Let 0P , 1P be values of the
NHPP at two specifications of b say 0 1,b b where ( )0 1b b< respectively. It can be shown that for
our models ( )m t at 1b is greater than that at 0b . Symbolically ( ) ( )0 1m t m t< . Then the SPRT
procedure is as follows:
Accept the system to be reliable
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
77
1
0
P
B
P
≤
i.e.,
[ ]
[ ]
1
0
( )( )
1
( )( )
0
. ( )
. ( )
N tm t
N tm t
e m t
B
e m t
−
−
≤
i.e.,
1 0
1 0
log ( ) ( )
1
( )
log ( ) log ( )
m t m t
N t
m t m t
β
α
+ −
− ≤
−
(3.1)
Decide the system to be unreliable and reject if
1
0
P
A
P
≥
i.e.,
1 0
1 0
1
log ( ) ( )
( )
log ( ) log ( )
m t m t
N t
m t m t
β
α
−
+ −
≥
−
(3.2)
Continue the test procedure as long as
1 0 1 0
1 0 1 0
1
log ( ) ( ) log ( ) ( )
1
( )
log ( ) log ( ) log ( ) log ( )
m t m t m t m t
N t
m t m t m t m t
β β
α α
−
+ − + −
− < <
− −
(3.3)
Substituting the appropriate expressions of the respective mean value function – m(t) of Rayleigh
we get the respective decision rules and are given in followings lines
Acceptance region:
( ) ( )
( )
( )
( )
2 2
0 1
2
1
2
0
log
1
( )
1
log
1
b t b t
b t
b t
a e e
N t
e
e
β
α
− −
−
−
+ −
− ≤
−
−
(3.4)
Rejection region:
( ) ( )
( )
( )
( )
2 2
0 1
2
1
2
0
1
log
( )
1
log
1
b t b t
b t
b t
a e e
N t
e
e
β
α
− −
−
−
−
+ −
≥
−
−
(3.5)
Continuation region:
( ) ( )
( )
( )
( )
( )
( ) ( )
( )
( )
( )
2 22 2
0 01 1
2 2
1 1
2 2
0 0
1
log log
1
1 1
log log
1 1
b t b tb t b t
b t b t
b t b t
a e e a e e
N t
e e
e e
β β
α α
− −− −
− −
− −
−
+ − + −
− < <
− −
− −
(3.6)
It may be noted that in the above model the decision rules are exclusively based on the strength of
the sequential procedure (α,β ) and the values of the respective mean value functions namely,
0 ( )m t , 1( )m t . If the mean value function is linear in ‘t’ passing through origin, that is, m(t) = λt
the decision rules become decision lines as described by Stieber (1997). In that sense equations
6. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
78
(3.1), (3.2) , (3.3) can be regarded as generalizations to the decision procedure of Stieber (1997).
The applications of these results for live software failure data are presented with analysis in
Section 5.
4. ML (MAXIMUM LIKELIHOOD) PARAMETER ESTIMATION
The idea behind maximum likelihood parameter estimation is to determine the parameters that
maximize the probability (likelihood) of the sample data. The method of maximum likelihood is
considered to be more robust (with some exceptions) and yields estimators with good statistical
properties. In other words, MLE methods are versatile and apply to many models and to different
types of data. Although the methodology for maximum likelihood estimation is simple, the
implementation is mathematically intense. Using today's computer power, however, mathematical
complexity is not a big obstacle. If we conduct an experiment and obtain N independent
observations, 1 2, , , Nt t tK . Then the likelihood function is given by[2] the following product:
( )1 2 1 2 1 2
1
, , , | , , , ( ; , , , )
N
N k i k
i
L t t t L f tθ θ θ θ θ θ
=
= = ∏K K K
Likely hood function by using λ(t) is: L =
1
( )
n
i
i
tλ
=
∏
The logarithmic likelihood function is given by: 1 2
1
ln ln ( ; , , , )
N
i k
i
L f t θ θ θ
=
Λ = = ∑ K
Log L = log (
1
( )
n
i
i
tλ
=
∏ )
which can be written as [ ]
1
log ( ) ( )
n
i n
i
t m tλ
=
−∑
The maximum likelihood estimators (MLE) of 1 2, , , kθ θ θK are obtained by maximizing L or Λ ,
where Λ is ln L . By maximizing , which is much easier to work with than L, the maximum
likelihood estimators (MLE) of 1 2, , , kθ θ θK are the simultaneous solutions of k equations such
that: ( )
0
jθ
∂ Λ
=
∂
, j=1,2,…,k
The parameters ‘a’ and ‘b’ are estimated using iterative Newton Raphson Method, which is given
as 1
( )
'( )
n
n n
n
g x
x x
g x
+ = −
For the present model of Weibull, the parameters are estimated from [9].
5. SPRT ANALYSIS OF LIVE DATA SETS
We see that the developed SPRT methodology is for a software failure data which is of the form
[t, N(t)] where N(t) is the failure number of software system or its sub system in ‘t’ units of time.
In this section we evaluate the decision rules based on the considered mean value function for
Five different data sets of the above form, borrowed from [2][7][8] and SONATA software
services. Based on the estimates of the parameter ‘b’ in each mean value function, we have
chosen the specifications of 0b b δ= − , 1b b δ= + equidistant on either side of estimate of b
obtained through a data set to apply SPRT such that b0 < b < b1. Assuming the value of
0.0025δ = , the choices are given in the following table.
Table 1: Estimates of a,b & Specifications of b0, b1
Data Set
Estimate of
‘a’
Estimate of
‘b’
b0 b1
Xie 30.05159 0.003416 0.000916 0.005916
AT&T 23.71966 0.004824 0.002324 0.007324
7. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
79
IBM 19.16436 0.00711 0.00461 0.00961
Lyu 24.086392 0.033562 0.031062 0.036062
NTDS 28.85193 0.011827 0.009327 0.014327
SONATA 31.961497 0.000912 -0.001588 0.003412
TROPICO-R 33.966286 0.004934 0.002434 0.007434
Using the selected 0b , 1b and subsequently the 0 1( ), ( )m t m t for the model, we calculated the
decision rules given by Equations 3.1, 3.2, sequentially at each ‘t’ of the data sets taking the
strength ( α, β ) as (0.05, 0.2). These are presented for the model in Table 2.
Table 2: SPRT analysis for 7 data sets
Data Set T N(t)
R.H.S of equation
(5.3.10)
Acceptance region (≤)
R.H.S of Equation
(5.3.11)
Rejection Region(≥)
Decision
Xie 30.02 1 -0.17291773 0.98310581 Rejection
AT & T
5.5 1 -0.66343858 1.22238877
Rejection
7.33 2 -0.6516082 1.23375403
IBM
10 1 -0.96601265 1.97464106
Rejection19 2 -0.72618077 2.19598203
32 3 -0.16030739 2.71551971
Lyu
0.5 1 -5.2122577 9.29358823
Rejection
1.7 2 -5.13340617 9.35093377
4.5 3 -4.63088682 9.71308540
7.2 4 -3.78455807 10.308599
10 5 -2.64978817 11.0721445
13 6 -1.31134889 11.9012488
14.8 7 -0.52315658 12.335913
15.7 8 -0.14781485 12.5227584
17.1 9 0.39937518 12.7629428
20.6 10 1.50499336 13.041212
24 11 2.14640728 12.8236973
25.2 12 2.26736443 12.6344422
26.1 13 2.32374036 12.4570439
NTDS
9 1 -1.48853384 3.52816085
Rejection
21 2 -0.16621278 4.72824732
32 3 1.57766914 6.2822846
36 4 2.2450846 6.86442913
43 5 3.35860135 7.81077962
45 6 3.65218168 8.05284578
50 7 4.31762368 8.58317713
58 8 5.13176726 9.16554443
63 9 5.46268271 9.34426677
70 10 5.6895861 9.35204023
SONATA
52.5 1 -0.49942268 2.30862835
Acceptance
105 2 0.89550118 3.63547513
131.25 3 1.7959382 4.48617512
183.75 4 3.68180303 6.24476432
201.25 5 4.26792637 6.78119865
306.25 6 6.45927614 8.62012162
8. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4, August 2011
80
TROPICO-R
1500
7 1 -0.6606864 1.27763909
Rejection
8 2 -0.64936629 1.28863802
From the above table we see that a decision either to accept or reject the system is reached much
in advance of the last time instant of the data(the testing time).
6. CONCLUSION
The table 2 shows that Weibull model as exemplified for 7 Data Sets indicate that the model is
performing well in arriving at a decision. Out of 7 Data Sets the procedure applied on the model
has given a decision of rejection for 6, acceptance for 1 and continue for none at various time
instant of the data as follows. DS1, DS2, DS3, DS4, DS5, DS7 are rejected at 1st
, 2nd
, 3rd
,13th
,10th
and 2nd
instant of time respectively. DS6 is accepted at 2nd
instant of time. Therefore, we
may conclude that, applying SPRT on data sets we can come to an early conclusion of reliable /
unreliable of software.
7. REFERENCES
[1] GOEL, A.L and OKUMOTO, K. “A Time Dependent Error Detection Rate Model For Software
Reliability And Other Performance Measures”, IEEE Transactions on Reliability, vol.R-28,
pp.206-211, 1979.
[2] Pham. H., “System software reliability”, Springer. 2006.
[3] Satya Prasad, R., ”Half logistic Software reliability growth model “, 2007, Ph.D Thesis of ANU,
India.
[4] Wald. A., “Sequential Analysis”, John Wiley and Son, Inc, New York. 1947.
[5] STIEBER, H.A. “Statistical Quality Control: How To Detect Unreliable Software Components”,
Proceedings the 8th
International Symposium on Software Reliability Engineering, 8-12. 1997.
[6] WOOD, A. “Predicting Software Reliability”, IEEE Computer, 2253-2264. 1996.
[7] Xie, M., Goh. T.N., Ranjan.P., “Some effective control chart procedures for reliability
monitoring” -Reliability engineering and System Safety 77 143 -150¸ 2002.
[8] Michael. R. Lyu, “The hand book of software reliability engineering”, McGrawHill & IEEE
Computer Society press.
[9] Dr R.Satya Prasad, G.Krishna Mohan and Prof R R L Kantham. Article: Time Domain based
Software Process Control using Weibull Mean Value Function. International Journal of Computer
Applications 18(3):18-21, March 2011. .
Author Profile:
First Author:
Mr. G. Krishna Mohan is working as a Reader in the Department of Computer Science,
P.B.Siddhartha College, Vijayawada. He obtained his M.C.A degree from Acharya
Nagarjuna University in 2000, M.Tech from JNTU, Kakinada, M.Phil from Madurai
Kamaraj University and pursuing Ph.D at Acharya Nagarjuna University. His research
interests lies in Data Mining and Software Engineering.
Second Author:
Dr. R. Satya Prasad received Ph.D. degree in Computer Science in the faculty of
Engineering in 2007 from Acharya Nagarjuna University, Andhra Pradesh. He received
gold medal from Acharya Nagarjuna University for his outstanding performance in Masters
Degree. He is currently working as Associate Professor and H.O.D, in the Department of
Computer Science & Engineering, Acharya Nagarjuna University. His current research is
focused on Software Engineering. He has published several papers in National &
International Journals.