2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)
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9781538659205

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Ensemble techniques for software change prediction: A preliminary investigation

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368455 ◽

2018 ◽

Cited By ~ 4

Author(s):

Gemma Catolino ◽

Filomena Ferrucci

Keyword(s):

Preliminary Investigation ◽

Ensemble Techniques ◽

Software Change ◽

Change Prediction

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Author index

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368448 ◽

2018 ◽

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Co-evolution analysis of production and test code by learning association rules of changes

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368456 ◽

2018 ◽

Cited By ~ 4

Author(s):

Laszlo Vidacs ◽

Martin Pinzger

Keyword(s):

Association Rules ◽

Evolution Analysis

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Machine learning-based run-time anomaly detection in software systems: An industrial evaluation

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368453 ◽

2018 ◽

Cited By ~ 3

Author(s):

Fabian Huch ◽

Mojdeh Golagha ◽

Ana Petrovska ◽

Alexander Krauss

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Software Systems ◽

Run Time

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2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368449 ◽

2018 ◽

Download Full-text

ConfigFile++: Automatic comment enhancement for misconfiguration prevention

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368457 ◽

2018 ◽

Author(s):

Yuanliang Zhang ◽

Shanshan Li ◽

Xiangyang Xu ◽

Xiangke Liao ◽

Shazhou Yang ◽

...

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How high will it be? Using machine learning models to predict branch coverage in automated testing

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368454 ◽

2018 ◽

Cited By ~ 3

Author(s):

Giovanni Grano ◽

Timofey V. Titov ◽

Sebastiano Panichella ◽

Harald C. Gall

Keyword(s):

Machine Learning ◽

Automated Testing ◽

Learning Models ◽

Branch Coverage ◽

Machine Learning Models

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Message from the chairs

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368450 ◽

2018 ◽

Keyword(s):

The Chairs

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[Title page]

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368447 ◽

2018 ◽

Keyword(s):

Title Page

Download Full-text

The role of meta-learners in the adaptive selection of classifiers

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) ◽

10.1109/maltesque.2018.8368452 ◽

2018 ◽

Author(s):

Dario Di Nucci ◽

Andrea De Lucia

Keyword(s):

Adaptive Selection ◽

Selection Of

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2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)
Latest Publications

TOTAL DOCUMENTS

H-INDEX

Published By IEEE

Ensemble techniques for software change prediction: A preliminary investigation

Author index

Co-evolution analysis of production and test code by learning association rules of changes

Machine learning-based run-time anomaly detection in software systems: An industrial evaluation

Contents

ConfigFile++: Automatic comment enhancement for misconfiguration prevention

How high will it be? Using machine learning models to predict branch coverage in automated testing

Message from the chairs

[Title page]

The role of meta-learners in the adaptive selection of classifiers

Export Citation Format

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)Latest Publications

TOTAL DOCUMENTS

H-INDEX

Published By IEEE

Ensemble techniques for software change prediction: A preliminary investigation

Author index

Co-evolution analysis of production and test code by learning association rules of changes

Machine learning-based run-time anomaly detection in software systems: An industrial evaluation

Contents

ConfigFile++: Automatic comment enhancement for misconfiguration prevention

How high will it be? Using machine learning models to predict branch coverage in automated testing

Message from the chairs

[Title page]

The role of meta-learners in the adaptive selection of classifiers

2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)
Latest Publications