Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

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Designing predictive maintenance systems using decision tree-based machine learning techniques

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-04-2019-0131 ◽

2020 ◽

Vol 37 (4) ◽

pp. 659-686 ◽

Cited By ~ 2

Author(s):

Shashidhar Kaparthi ◽

Daniel Bumblauskas

Keyword(s):

Machine Learning ◽

Decision Making ◽

Decision Tree ◽

Service Management ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Content Type ◽

Learning Techniques ◽

Maintenance Systems

PurposeThe after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.Design/methodology/approachWe propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.FindingsWe found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.Research limitations/implicationsThis approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.Practical implicationsThis approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.Social implicationsSustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.Originality/valueThis is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

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On the Application of Cross-Project Validation for Predicting Maintainability of Open Source Software using Machine Learning Techniques

2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) ◽

10.1109/icrito.2018.8748749 ◽

2018 ◽

Cited By ~ 2

Author(s):

Ruchika Malhotra ◽

Kusum Lata

Keyword(s):

Machine Learning ◽

Open Source ◽

Open Source Software ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Cross Project

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Collecting Vulnerable Source Code from Open-Source Repositories for Dataset Generation

Applied Sciences ◽

10.3390/app10041270 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1270

Author(s):

Razvan Raducu ◽

Gonzalo Esteban ◽

Francisco J. Rodríguez Lera ◽

Camino Fernández

Keyword(s):

Machine Learning ◽

Open Source ◽

Descriptive Analysis ◽

Source Code ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Current Status ◽

Software Vulnerabilities ◽

Learning Techniques ◽

Public Repositories

Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human intervention. However, these algorithms require data-driven engines based on the processing of huge amounts of data, known as datasets. This paper introduces the SonarCloud Vulnerable Code Prospector for C (SVCP4C). This tool aims to collect vulnerable source code from open source repositories linked to SonarCloud, an online tool that performs static analysis and tags the potentially vulnerable code. The tool provides a set of tagged files suitable for extracting features and creating training datasets for Machine Learning algorithms. This study presents a descriptive analysis of these files and overviews current status of C vulnerabilities, specifically buffer overflow, in the reviewed public repositories.

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FIBR-OSS: fault injection model for bug reports in open-source software

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i1.pp465-474 ◽

2020 ◽

Vol 20 (1) ◽

pp. 465

Author(s):

Sundos Abdulameer Alazawi ◽

Mohammed Najim Al-Salam

Keyword(s):

Machine Learning ◽

Open Source ◽

Open Source Software ◽

Fault Injection ◽

Failure Prediction ◽

Machine Learning Techniques ◽

Significant Advance ◽

Bug Reports ◽

Learning Techniques ◽

Injection Model

<span>For assessment of system dependability, fault injection techniques are used to expedite the presence of an error or failure in the system, which helps evaluate fault tolerance and system failure prediction. Defects classification and prediction is the principal significant advance in the trustworthiness evaluation of complex software systems such as open-source software since it can quickly be affected by the reliability of those systems, improves performance, and lessening the product cost. In this context, a new prototype of the fault injection model is presented, FIBR-OSS (Fault Injection for Bug Reports in Open-Source Software). FIBR-OSS can support developers to evaluate the system performance during phase's development for its dependability attributes such as reliability and system dependability means such as fault prediction or forecasting. FIBR-OSS is used for fault speed-up to test the system's failure prediction performance. Some machine learning techniques are implemented on bug reports produced existing by the bug tracking system as datasets for failure prediction techniques, some of those machine learning techniques are used in our approach.</span>

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Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software

CIN Computers Informatics Nursing ◽

10.1097/cin.0000000000000173 ◽

2015 ◽

Vol 33 (8) ◽

pp. 368-377 ◽

Cited By ~ 2

Author(s):

ALEXANDER ZLOTNIK ◽

ASCENSIÓN GALLARDO-ANTOLÍN ◽

MIGUEL CUCHÍ ALFARO ◽

MARÍA CARMEN PÉREZ PÉREZ ◽

JUAN MANUEL MONTERO MARTÍNEZ

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Open Source ◽

Emergency Department Visit ◽

Nursing Staff ◽

Open Source Software ◽

Machine Learning Techniques ◽

Staff Allocation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0177 ◽

2017 ◽

Vol 7 (7) ◽

pp. 172

Author(s):

Anantvir Singh Romana

Keyword(s):

Machine Learning ◽

Subsequent Treatment ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Disease Prediction ◽

Classification Problems ◽

Learning Techniques ◽

Neural Network Classifiers ◽

Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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