Predicting Change Prone Classes in Open Source Software

2018 ◽  
Vol 8 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Deepa Godara ◽  
Amit Choudhary ◽  
Rakesh Kumar Singh
Keyword(s):  
Prediction Model ◽  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
New Technology ◽  
Modern Technology ◽  
Time Frequency ◽  
Rigorous Testing ◽  
Technology Changes ◽  
Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

Predicting Change Prone Classes in Open Source Software

2021 ◽  
pp. 653-675
Author(s):  
Deepa Godara ◽  
Amit Choudhary ◽  
Rakesh Kumar Singh
Keyword(s):  
Prediction Model ◽  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
New Technology ◽  
Modern Technology ◽  
Software Life Cycle ◽  
Rigorous Testing ◽  
Technology Changes ◽  
Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

Demography of Open Source Software Prediction Models and Techniques

2021 ◽  
pp. 620-652
Author(s):  
Kaniz Fatema ◽  
M. M. Mahbubul Syeed ◽  
Imed Hammouda
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Preventive Maintenance ◽  
Prediction Models ◽  
Journal Article ◽  
Literature Survey ◽  
Future Research ◽  
Fundamental Knowledge ◽  
Future Research Directions ◽  
Insight Into

Open source software (OSS) is currently a widely adopted approach to developing and distributing software. Many commercial companies are using OSS components as part of their product development. For instance, more than 58% of web servers are using an OSS web server, Apache. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This chapter reports on a systematic literature survey aimed at the identification and structuring of research that offers prediction models and techniques in analysing OSS projects. The study outcome provides insight into what constitutes the main contributions of the field, identifies gaps and opportunities, and distils several important future research directions. This chapter extends the authors' earlier journal article and offers the following improvements: broader study period, enhanced discussion, and synthesis of reported results.

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

2020 ◽  
Vol 234 (13) ◽  
pp. 1580-1597
Author(s):  
Minghui Cheng ◽  
Li Jiao ◽  
Xuechun Shi ◽  
Xibin Wang ◽  
Pei Yan ◽  
...  
Keyword(s):  
Tool Wear ◽  
Prediction Model ◽  
Cutting Force ◽  
Surface Quality ◽  
Surface Texture ◽  
High Strength Steel ◽  
Prediction Models ◽  
High Strength ◽  
Support Vector ◽  
Time Frequency

In the process of high strength steel turning, tool wear will reduce the surface quality of the workpiece and increase cutting force and cutting temperature. To obtain the fine surface quality and avoid unnecessary loss, it is necessary to monitor the state of tool wear in the dry turning. In this article, the cutting force, vibration signal and surface texture of the machined surface were collected by tool condition monitoring system and signal processing techniques are being used for extracting the time-domain, frequency-domain and time-frequency features of cutting force and vibration. The gray level processing technique is used to extract the features of the gray co-occurrence matrix of the surface texture and found that these features changed simultaneously when the cutting tool broke. After this, an intelligent prediction model of tool wear was built using the support vector regression (SVR) whose kernel function parameters were optimized by the grid search algorithm (GS), the genetic algorithm (GA) and the particle swarm optimization algorithm respectively. The features extracted from the signals and surface texture are used to train the prediction model in MATLAB. It was found that after the surface texture features were fused using the intelligent prediction model on the basis of the features of cutting force and vibration, prediction accuracy of the proposed method is found as 97.32% and 96.72% respectively under the two prediction models of GA-SVR and GS-SVR. Moreover, the intelligent prediction model can not only predict the tool wear under different cutting conditions, but also the different wear stages in a single wear cycle and the absolute error between the predicted value and the actual value is less than 10 μm, the confidence coefficient of prediction curve is around 0.99.

Aplicativos Educacionais para Smartphone e sua Integração Com o Ensino de Química

2016 ◽  
Vol 17 (5) ◽  
pp. 398
Author(s):  
Marcella Cristyanne Comar Gresczysczyn ◽  
Paulo Sérgio de Camargo Filho ◽  
Eduardo Lemes Monteiro
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Digital Technology ◽  
Chemistry Education ◽  
New Technology ◽  
Great Promise ◽  
Teaching Chemistry ◽  
Semiotic Representations ◽  
Education Improvement ◽  
Google Play

A tecnologia digital é uma grande promessa para o ensino de Química na escola, acredita-se que a implementação dessas tecnologias produz melhoria na educação. No entanto, pouco se sabe sobre a articulação coordenada dos aplicativos para smartphone propostos para este fim e as demais representações semióticas tradicionais da Ciência e efeitos sobre a aprendizagem dos alunos. É importante acrescentar que a escola ainda não conseguiu integrar todas as mudanças da sociedade com a rápida evolução das tecnologias, afastando-se dos jovens inseridos nessa evolução. Ao professor se exige um esforço para a readaptação a essa integração, o papel desse educador deverá ser ativo e responsável no enquadramento pedagógico das tecnologias, para que possa tornar-se um meio de renovação do ensino e não apenas um mero reforço de práticas tradicionais. Atualmente, na área da Educação Química, nota-se que a informatização e os aplicativos tão acessíveis a qualquer classe da população, podem proporcionar situações de aprendizagem que acabavam restritas, pelo alto custo. Nesse contexto e, com o objetivo de conhecer os aplicativos para a Educação Química e o crescimento de sua oferta para incentivar sua adoção na educação, são trazidos, nesse artigo, os resultados de uma pesquisa sobre aplicativos para Android® relacionados à área, realizada a partir da busca de aplicativos em repositórios livres tais como Free and Open Source Software - FOSS® e Google Play® disponíveis em um período de 2012 a 2016, apresentando a evolução do número de aplicativos disponíveis, os temas mais recorrentes e indicando aplicativos para a Educação Química. Palavras-chave: Ensino. Química. Aplicativos. Smartphone. Tecnologia Digital AbstractThe use of digital technology holds great promise for teaching chemistry in school, it is believed that the implementation of this new technology produces education improvement. However, little is known about the coordinated joint applications for smartphone proposed for this objective and other traditional Sciences semiotic representations and effects on the students’ learning. It is important to add that the school was still not able so far to integrate all the changes in society with the rapid technology evolution, moving away from our young people who are inserted into this evolution. To the teacher, it is required an effort to rehabilitate this integration, the educator’s role should be active and responsible in the technologies educational environment, so they can become a means of teaching renewal and not just a mere reinforcement of traditional practices. Currently, in the Chemical Education area, it is noted that the computerization and applications so accessible to any class population can provide learning situations which used to be limited due to theirhigh cost. In this context and in order to knowthe Chemistry Education applications and the growth supply to encourage their adoption in education, are coveredin this article, the results of a survey on applications for Android® related to the area, accomplished from search applications for free repositories such as Free and Open Source Software - FOSS® and Google Play® available in a period from 2012 to 2016, showing the evolution of the number of available applications, the most recurrent themes and indicating applications for Chemical Education.Keywords: Education. Chemistry. Applications. Smartphone. Digital technology.

scholarly journals Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data

2018 ◽  
Vol 25 (8) ◽  
pp. 969-975 ◽  
Author(s):  
Jenna M Reps ◽  
Martijn J Schuemie ◽  
Marc A Suchard ◽  
Patrick B Ryan ◽  
Peter R Rijnbeek
Keyword(s):  
Open Source ◽  
Observational Data ◽  
Open Source Software ◽  
Data Model ◽  
Prediction Models ◽  
External Validation ◽  
Model Performance ◽  
Common Data Model ◽  
Proof Of Concept ◽  
Model Framework

Abstract Objective To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility. Methods Based on existing best practices we propose a 5 step standardized framework for: (1) transparently defining the problem; (2) selecting suitable datasets; (3) constructing variables from the observational data; (4) learning the predictive model; and (5) validating the model performance. We implemented this framework as open-source software utilizing the Observational Medical Outcomes Partnership Common Data Model to enable convenient sharing of models and reproduction of model evaluation across multiple observational datasets. The software implementation contains default covariates and classifiers but the framework enables customization and extension. Results As a proof-of-concept, demonstrating the transparency and ease of model dissemination using the software, we developed prediction models for 21 different outcomes within a target population of people suffering from depression across 4 observational databases. All 84 models are available in an accessible online repository to be implemented by anyone with access to an observational database in the Common Data Model format. Conclusions The proof-of-concept study illustrates the framework’s ability to develop reproducible models that can be readily shared and offers the potential to perform extensive external validation of models, and improve their likelihood of clinical uptake. In future work the framework will be applied to perform an “all-by-all” prediction analysis to assess the observational data prediction domain across numerous target populations, outcomes and time, and risk settings.

scholarly journals EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy

2022 ◽  
Vol 8 ◽  
Author(s):  
Bin Wang ◽  
Xiong Han ◽  
Zongya Zhao ◽  
Na Wang ◽  
Pan Zhao ◽  
...  
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Kolmogorov Complexity ◽  
Cross Validation ◽  
Prediction Models ◽  
Focal Epilepsy ◽  
Gradient Boosting ◽  
Newly Diagnosed ◽  
Sensitivity Specificity ◽  
Fold Cross Validation

Objective: Antiseizure medicine (ASM) is the first choice for patients with epilepsy. The choice of ASM is determined by the type of epilepsy or epileptic syndrome, which may not be suitable for certain patients. This initial choice of a particular drug affects the long-term prognosis of patients, so it is critical to select the appropriate ASMs based on the individual characteristics of a patient at the early stage of the disease. The purpose of this study is to develop a personalized prediction model to predict the probability of achieving seizure control in patients with focal epilepsy, which will help in providing a more precise initial medication to patients.Methods: Based on response to oxcarbazepine (OXC), enrolled patients were divided into two groups: seizure-free (52 patients), not seizure-free (NSF) (22 patients). We created models to predict patients' response to OXC monotherapy by combining Electroencephalogram (EEG) complexities and 15 clinical features. The prediction models were gradient boosting decision tree-Kolmogorov complexity (GBDT-KC) and gradient boosting decision tree-Lempel-Ziv complexity (GBDT-LZC). We also constructed two additional prediction models, support vector machine-Kolmogorov complexity (SVM-KC) and SVM-LZC, and these two models were compared with the GBDT models. The performance of the models was evaluated by calculating the accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC) of these models.Results: The mean accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-LZC model after five-fold cross-validation were 81%, 84%, 91%, 87%, 91%, 64%, 81%, respectively. The average accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-KC model with five-fold cross-validation were 82%, 84%, 92%, 88%, 83%, 92%, 83%, respectively. We used the rank of absolute weights to separately calculate the features that have the most significant impact on the classification of the two models.Conclusion: (1) The GBDT-KC model has the potential to be used in the clinic to predict seizure-free with OXC monotherapy. (2). Electroencephalogram complexity, especially Kolmogorov complexity (KC) may be a potential biomarker in predicting the treatment efficacy of OXC in newly diagnosed patients with focal epilepsy.

Enhancing Information Retrieval System Using Change-Prone Classes

2020 ◽  
pp. 40-68 ◽  
Author(s):  
Deepa Bura ◽  
Amit Choudhary
Keyword(s):  
Information Retrieval ◽  
Prediction Model ◽  
Experimental Analysis ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Time Frequency ◽  
Retrieval Systems ◽  
Efficient Information ◽  
Two Phases ◽  
Sensitivity Specificity

In today's competitive world, each company is required to change software to meet changing customer requirements. At the same time, an efficient information retrieval system is required as changes made to software in different versions can lead to complicated retrieval systems. This research aims to find the association between changes and object-oriented metrics using different versions of open source software. Earlier researchers have used various techniques such as statistical methods for the prediction of change-prone classes. This research uses execution time, frequency, run time information, popularity, and class dependency in prediction of change-prone classes. For evaluating the performance of the prediction model, sensitivity, specificity, and ROC curve are used. Higher values of AUC indicate the prediction model gives accurate results. Results are validated in two phases: Experimental Analysis I validates results using OpenClinic software and OpenHospital software and Experimental Analysis II validates result using Neuroph 2.9.2 and Neuroph 2.6.

Demography of Open Source Software Prediction Models and Techniques

2018 ◽  
pp. 24-56
Author(s):  
Kaniz Fatema ◽  
M. M. Mahbubul Syeed ◽  
Imed Hammouda
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Preventive Maintenance ◽  
Prediction Models ◽  
Journal Article ◽  
Literature Survey ◽  
Future Research ◽  
Fundamental Knowledge ◽  
Future Research Directions ◽  
Insight Into

Open source software (OSS) is currently a widely adopted approach to developing and distributing software. Many commercial companies are using OSS components as part of their product development. For instance, more than 58% of web servers are using an OSS web server, Apache. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This chapter reports on a systematic literature survey aimed at the identification and structuring of research that offers prediction models and techniques in analysing OSS projects. The study outcome provides insight into what constitutes the main contributions of the field, identifies gaps and opportunities, and distils several important future research directions. This chapter extends the authors' earlier journal article and offers the following improvements: broader study period, enhanced discussion, and synthesis of reported results.

scholarly journals FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Robert Oostenveld ◽  
Pascal Fries ◽  
Eric Maris ◽  
Jan-Mathijs Schoffelen
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Modular Design ◽  
Large Data ◽  
Data Sets ◽  
Time Frequency ◽  
Electrophysiological Data ◽  
Open Source Software Package ◽  
Advanced Analysis ◽  
High Level

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

Sign in / Sign up

Export Citation Format

Share Document