Machine learning techniques for software testing effort prediction

2021 ◽  
Author(s):  
Cuauhtémoc López-Martín
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Machine Learning Techniques ◽  
Testing Effort ◽  
Learning Techniques ◽  
Effort Prediction

A Unified Framework for Bug Report Assignment

2019 ◽  
Vol 29 (04) ◽  
pp. 607-628 ◽  
Author(s):  
Yuan Zhao ◽  
Tieke He ◽  
Zhenyu Chen
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Software Testing ◽  
Mobile Internet ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Unified Framework ◽  
Bug Reports ◽  
Learning Techniques ◽  
Bug Report

It is typically a manual, time-consuming, and tedious task of assigning bug reports to individual developers. Although some machine learning techniques are adopted to alleviate this dilemma, they are mainly focused on the open source projects, which use traditional repositories such as Bugzilla to manage their bug reports. With the boom of the mobile Internet, some new requirements and methods of software testing are emerging, especially the crowdsourced testing. Unlike the traditional channels, whose bug reports are often heavyweight, which means their bug reports are standardized with detailed attribute localization, bug reports tend to be lightweight in the context of crowdsourced testing. To exploit the differences of the bug reports assignment in the new settings, a unified bug reports assignment framework is proposed in this paper. This framework is capable of handling both the traditional heavyweight bug reports and the lightweight ones by (i) first preprocessing the bug reports and feature selections, (ii) then tuning the parameters that indicate the ratios of choosing different methods to vectorize bug reports, (iii) and finally applying classification algorithms to assign bug reports. Extensive experiments are conducted on three datasets to evaluate the proposed framework. The results indicate the applicability of the proposed framework, and also reveal the differences of bug report assignment between traditional repositories and crowdsourced ones.

scholarly journals Role of Machine Learning & Artificial Intelligence Techniques in Software Testing

2021 ◽  
Vol 12 (6) ◽  
pp. 2913-2921
Author(s):  
Nilofar Mulla, Dr. Naveenkumar Jayakumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Software Testing ◽  
Machine Learning Techniques ◽  
Business Growth ◽  
Growth Machine ◽  
Learning Techniques ◽  
Test Suites ◽  
Applications Of Ai

This study provides information about the use of artificial intelligence (AI) and machine learning (ML) techniques in the field of software testing. The use of AI in software testing is still in its initial stages. Also the automation level is lesser compared to more evolved areas of work.AI and ML can be used to help reduce tediousness and automate tasks in software testing. Testing can be made more efficient and smarter with the help of AI. Researchers recognize potential of AI to bridge the gap between human and machine driven testing capabilities. There are still number of challenges to fully utilize AI and ML techniques in testing but it will definitely enhance the entire testing process and skills of testers and will contribute in business growth. Machine learning research is a subset of overall AI research. The life-cycle of software is increasingly shortening and becoming more complicated. There is a struggle in software development between the competing pressures of developing software and meeting deadlines. AI-powered automated testing makes conducting full test suites in a timely manner on every change. In this article a detailed overview about the various applications of AI in software testing have been demonstrated. Also the implementation of machine learning in software testing has been discussed in detail and use of different machine learning techniques has been explained as well.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

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.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

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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