Predicting Software Abnormal State by using Classification Algorithm

2016 ◽  
Vol 27 (2) ◽  
pp. 49-65 ◽  
Author(s):  
Yongquan Yan ◽  
Ping Guo
Keyword(s):  
Classification Algorithm ◽  
Software System ◽  
Selection Algorithm ◽  
Forward Selection ◽  
Software Application ◽  
Software Aging ◽  
Crash Failures ◽  
System Variables ◽  
A Minor ◽  
Fold Cross Validation

Software aging, also called smooth degradation or chronics, has been observed in a long running software application, accompanied by performance degradation, hang/crash failures or both. The key for software aging problem is how to fast and accurately detect software aging occurrence, which is a hard work due to the long delay before aging appearance. In this paper, two problems about software aging prediction are solved, which are how to accurately find proper running software system variables to represent system state and how to predict software aging state in a running software system with a minor error rate. Firstly, the authors use proposed stepwise forward selection algorithm and stepwise backward selection algorithm to find a proper subset of variables set. Secondly, a classification algorithm is used to model software aging process. Lastly, t-test with k-fold cross validation is used to compare performance of two classification algorithms. In the experiments, the authors find that their proposed method is an efficient way to forecast software aging problems in advance.

Predicting Software Abnormal State by using Classification Algorithm

2021 ◽  
pp. 1095-1113
Author(s):  
Yongquan Yan ◽  
Ping Guo
Keyword(s):  
Classification Algorithm ◽  
Software System ◽  
Selection Algorithm ◽  
Forward Selection ◽  
Software Application ◽  
Software Aging ◽  
Crash Failures ◽  
System Variables ◽  
A Minor ◽  
Fold Cross Validation

Software aging, also called smooth degradation or chronics, has been observed in a long running software application, accompanied by performance degradation, hang/crash failures or both. The key for software aging problem is how to fast and accurately detect software aging occurrence, which is a hard work due to the long delay before aging appearance. In this paper, two problems about software aging prediction are solved, which are how to accurately find proper running software system variables to represent system state and how to predict software aging state in a running software system with a minor error rate. Firstly, the authors use proposed stepwise forward selection algorithm and stepwise backward selection algorithm to find a proper subset of variables set. Secondly, a classification algorithm is used to model software aging process. Lastly, t-test with k-fold cross validation is used to compare performance of two classification algorithms. In the experiments, the authors find that their proposed method is an efficient way to forecast software aging problems in advance.

A Local Approach and Comparison with Other Data Mining Approaches in Software Application

2017 ◽  
pp. 1-26
Author(s):  
QingE Wu ◽  
Weidong Yang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Forecast Accuracy ◽  
Software System ◽  
Local Approach ◽  
Test Analysis ◽  
Software Application ◽  
Software Aging ◽  
Optimum Approach ◽  
Sensitivity Specificity

In order to complete an online, real-time and effective aging detection to software, this paper studies a local approach that is also called a fuzzy incomplete and a statistical data mining approaches, and gives their algorithm implementation in the software system fault diagnosis. The application comparison of the two data mining approaches with four classical data mining approaches in software system fault diagnosis is discussed. The performance of each approach is evaluated from the sensitivity, specificity, accuracy rate, error classified rate, missed classified rate, and run-time. An optimum approach is chosen from several approaches to do comparative study. On the data of 1020 samples, the operating results show that the fuzzy incomplete approach has the highest sensitivity, the forecast accuracy that are 96.13% and 94.71%, respectively, which is higher than those of other approaches. It has also the relatively less error classified rate is or so 4.12%, the least missed classified rate is or so 1.18%, and the least runtime is 0.35s, which all are less than those of the other approaches. After the performance, indices are all evaluated and synthesized, the results indicate the performance of the fuzzy incomplete approach is best. Moreover, from the test analysis known, the fuzzy incomplete approach has also some advantages, such as it has the faster detection speed, the lower storage capacity, and does not need any prior information in addition to data processing. These results indicate that the mining approach is more effective and feasible than the old data mining approaches in software aging detection.

scholarly journals IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

2007 ◽  
Vol 4 (3) ◽  
pp. 186-197 ◽  
Author(s):  
Jan Baumbach ◽  
Alexander Bunkowski ◽  
Sita Lange ◽  
Timm Oberwahrenbrock ◽  
Nils Kleinbölting ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Software System ◽  
Medical Software ◽  
Cancer Types ◽  
The One ◽  
Lung Cancer Detection ◽  
Processing Steps ◽  
Fold Cross Validation

Abstract IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world.After reviewing the IMS technology, we first describe the software architecture of IMS2 and then the integrated classification module, including necessary pre-processing steps and different classification methods. The Lung Hospital Hemer (Germany) provided IMS data of 35 patients suffering from lung cancer and 72 samples of healthy persons. IMS2 correctly classifies 99% of the samples, evaluated using 10-fold cross-validation.

A SVM QoS Estimation Model for the Component-Based Software System

2014 ◽  
Vol 631-632 ◽  
pp. 991-994
Author(s):  
Peng Nie
Keyword(s):  
Estimation Accuracy ◽  
Software System ◽  
Estimation Model ◽  
Software Application ◽  
Component Integration

More software application are intend to be developed by the automated code synthesis or the component integration. We proposed a SVM QoS estimation model for the component-based software system (SVMQEM). Our estimation model learns various system QoS properties and outputs a comprehensive QoS by SVM, which is different from the approaches available in the literature. The experiments shows that the SVMQEM has an acceptable QoS estimation accuracy for the component-based software system.

scholarly journals IMPLEMENTATION OF C4.5 METHOD TO DETERMINE THE FACTOR OF BEING LATE FOR COMING TO SCHOOL

2020 ◽  
Vol 2 (3) ◽  
pp. 115-120
Author(s):  
Cendana Puspitasari
Keyword(s):  
High School ◽  
Junior High School ◽  
Junior High ◽  
Common Factors ◽  
Selection Method ◽  
Classification Algorithm ◽  
Training Data ◽  
Questionnaire Data ◽  
Forward Selection ◽  
Class Viii

Being late to school is a deviant act that violates the rules or regulations in the school both written and unwritten. The discipline of students coming to school is the first to see, some common factors that occur delays can occur include: distance to school, hours of wake up, hours of departure, travel conditions, and vehicles used. In this study, the authors used the Classification algorithm with the C4.5 method and the Forward selection method. The sample used was questionnaire data for students of class VIII (eight) in State Junior High School 271 totaling 270 students. Using training data, certain attributes are determined to form a classifier model. The results of this study are the results of the accuracy of the C4.5 method of 60.74% with the results of the tree showing congestion is a factor of school delay and the results of the accuracy of 65.93% for Forward selection and get the 3 best attributes.

scholarly journals CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT

Biology ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Si-Yuan Lu ◽  
Zheng Zhang ◽  
Yu-Dong Zhang ◽  
Shui-Hua Wang
Keyword(s):  
Extreme Learning Machine ◽  
Cross Validation ◽  
Graph Model ◽  
Chest Ct ◽  
Selection Algorithm ◽  
Efficient Tool ◽  
K Nearest Neighbors ◽  
Average Accuracy ◽  
Learning Machine ◽  
Fold Cross Validation

Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis.

scholarly journals Software - Implemented Self-healing System

2007 ◽  
Vol 10 (2) ◽  
Author(s):  
Goutam Kumar Saha
Keyword(s):  
Fault Tolerance ◽  
Error Detection ◽  
Web Application ◽  
Fault Model ◽  
System Response ◽  
Software System ◽  
Actual Behavior ◽  
Self Healing ◽  
Software Application ◽  
Healing System

The term “Self-healing” denotes the capability of a software system in dealing with bugs. Fault tolerance for dependable computing is to provide the specified service through rigorous design whereas self-healing is meant for run-time issues. The paper describes various issues on designing a self-healing software application system that relies on the on-the-fly error detection and repair of web application or service agent code and data. Self-Healing is a very new area of research that deals with fault tolerance for dynamic systems. Self-healing deals with imprecise specification, uncontrolled environment and reconfiguration of system according to its dynamics. Software, which is capable of detecting and reacting to its malfunctions, is called self-healing software. Such software system has the ability to examine its failures and to take appropriate corrections. Self-Healing system must have knowledge about its expected behavior in order to examine whether its actual behavior deviates from its expected behavior in relation of the environment. A fault-model of Self-Healing system is to state what faults or injuries to be self-healed including fault duration, fault source such as, operational errors, defective system requirements or implementation errors etc. Self-healing categories of aspects include fault-model or fault hypothesis, System-response, System-completeness and Design-context. Based on many important literatures, this paper aims also to illustrate critical points of the emergent research topic of Self – Healing Software System.

ERP Software Maintenance

2020 ◽  
pp. 307-329
Author(s):  
Elyjoy Muthoni Micheni
Keyword(s):  
Software Maintenance ◽  
Cost Effective ◽  
Software System ◽  
Improve Performance ◽  
Help Desk ◽  
Software Application ◽  
Erp System ◽  
Process Support ◽  
Ieee Standard ◽  
Erp Software

This chapter will explain ERP software maintenance and the effort required to locate and fix errors in the ERP software. Software maintenance is defined as the totality of activities required to provide cost-effective support to a software system. The purpose of software maintenance is to modify and update software application after delivery to correct faults and to improve performance. The chapter will highlight activities performed during the pre-delivery stage, including planning for post-delivery operations, supportability, and logistics determination, and also activities performed during the post-delivery stage, including software modification, training, and operating a help desk. The chapter will discuss the types of maintenance and highlight the ERP process support activities and the ERP system maintainability framework. The chapter will explain the maintenance of ERP software and will also discuss the ISO/IEC 9126 and IEEE Standard 1219-1998 for software maintenance. Issues in ERP software maintenance are also presented and discussed.

Sign in / Sign up

Export Citation Format

Share Document