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Software Engineering for Machine Learning: A Case Study

Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, Thomas Zimmermann
2019 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)  
We use the phrase "working with AI" to mean working on any aspect of a product, feature, or service that has an AI component, including software engineering and development of the product or feature, data  ...  In this survey, we use the term "AI" broadly to refer to any form of artificial intelligence, machine learning, or statistical modeling.  ...  Running a machine learning algorithm over the data features including choosing good values for any additional parameters the algorithm requires.] Model evaluation. [What is this?  ... 
doi:10.1109/icse-seip.2019.00042 dblp:conf/icse/AmershiBBDGKNN019 fatcat:wo5nxkb3dbfmtjrtoepibvaoz4

Machine learning for software engineering

Karl Meinke, Amel Bennaceur
2018 Proceedings of the 40th International Conference on Software Engineering Companion Proceeedings - ICSE '18  
Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data.  ...  CCS CONCEPTS • Computing methodologies → Machine learning; • Software and its engineering → Model-driven software engineering;  ...  Meinke has a publication track record in the areas of machine learning for finite and infinite state systems, theoretical principles of learning-based testing, and practical tools and case studies for  ... 
doi:10.1145/3183440.3183461 dblp:conf/icse/MeinkeB18 fatcat:7c55s5vjb5eohdzb7ei67zqviy

PRACTICAL MACHINE LEARNING FOR SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING [chapter]

TIM MENZIES
2001 Handbook of Software Engineering and Knowledge Engineering  
Machine learning is practical for software engineering problems, even in datastarved domains.  ...  Machine learners automatically generate summaries of data or existing systems in a smaller form. Software engineers can use machine learners to simplify systems development.  ...  Machine learning that supports software engineering or knowl- Figure 3 : Predicting fault-prone modules [16] .  ... 
doi:10.1142/9789812389718_0035 fatcat:td656rynrngq7g32aelrdjezvy

A case study on machine learning model for code review expert system in software engineering

Michał Madera, Rafał Tomoń
2017 Proceedings of the 2017 Federated Conference on Computer Science and Information Systems  
In this paper we propose machine learning approach for pointing project artifacts that are significantly at risk of failure.  ...  Code review is a key tool for quality assurance in software development. It is intended to find coding mistakes overlooked during development phase and lower risk of bugs in final product.  ...  The paper is a case study on building Rework prediction system for one of the leading companies developing software for hospitals and medical laboratories around the world.  ... 
doi:10.15439/2017f536 dblp:conf/fedcsis/MaderaT17 fatcat:isntselskngspatvoqv3klhxua

Machine Learning for Software Engineering: A Systematic Mapping [article]

Saad Shafiq, Atif Mashkoor, Christoph Mayr-Dorn, Alexander Egyed
2020 arXiv   pre-print
Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software  ...  Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering.  ...  We also proposed a classification scheme in the form of the MLSE (machine learning for software engineering) taxonomy that highlights the overall applications of machine learning for software engineering  ... 
arXiv:2005.13299v1 fatcat:4ox7os6ebbh77bafi6ebi5jxf4

Analysis of Software Engineering for Agile Machine Learning Projects [article]

Kushal Singla, Joy Bose, Chetan Naik
2019 arXiv   pre-print
In this paper, we analyze project issues tracking data taken from Scrum (a popular tool for Agile) for several machine learning projects.  ...  After analyzing this data, we propose a few ways in which Agile machine learning projects can be better logged and executed, given their differences with normal software engineering projects.  ...  There are also a few studies of machine learning techniques applied to different use cases related to software engineering. The study by Wen et. al.  ... 
arXiv:1912.07323v1 fatcat:toi2jtn2erdobjum4enrda5byu

Studying Software Engineering Patterns for Designing Machine Learning Systems [article]

Hironori Washizaki, Hiromu Uchida, Foutse Khomh, Yann-Gael Gueheneuc
2019 arXiv   pre-print
Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering.  ...  Researchers and practitioners studying best practices for designing ML application systems and software to address the software complexity and quality of ML techniques.  ...  For the domain of software engineering for ML applications, case studies, practices, and patterns are available as independent documents.  ... 
arXiv:1910.04736v2 fatcat:za777dv5ujhqrei65gdsoitopa

Software engineering for artificial intelligence and machine learning software: A systematic literature review [article]

Elizamary Nascimento, Anh Nguyen-Duc, Ingrid Sundbø, Tayana Conte
2020 arXiv   pre-print
This study aims to investigate how software engineering (SE) has been applied in the development of AI/ML systems and identify challenges and practices that are applicable and determine whether they meet  ...  Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer  ...  We special thanks to the researcher Edson César for the support and collaboration during the conduct of this study. References  ... 
arXiv:2011.03751v1 fatcat:ec3p2ozwzzbhpf3nzu354bf72y

Learn&Fuzz: Machine learning for input fuzzing

Patrice Godefroid, Hila Peleg, Rishabh Singh
2017 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)  
We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF parser embedded in Microsoft's new Edge browser.  ...  In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques.  ...  We thank Dustin Duran and Mark Wodrich from the Microsoft Windows security team for their Edge-PDF-parser test-driver and for helpful feedback.  ... 
doi:10.1109/ase.2017.8115618 dblp:conf/kbse/GodefroidPS17 fatcat:xay6jh2tujgbplt4wsx74jbvua

Benchmarking Machine Learning Techniques for Software Defect Detection

Saiqa Aleem, Luiz Fernando Capretz, Faheem Ahmed
2015 International Journal of Software Engineering & Applications  
This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction.  ...  In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly.  ...  Jagath Samarabandu for his constructive comments which contributed to the improvement of this article as his course work.  ... 
doi:10.5121/ijsea.2015.6302 fatcat:72kebliurzfehpq775pgjotuye

Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure [article]

Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, Margaret Mitchell
2021 arXiv   pre-print
The framework uses the cyclical, infrastructural and engineering nature of dataset development to draw on best practices from the software development lifecycle.  ...  However the datasets which empower machine learning are often used, shared and re-used with little visibility into the processes of deliberation which led to their creation.  ...  INTRODUCTION Machine learning faces a crisis in accountability.  ... 
arXiv:2010.13561v2 fatcat:5gq5klvzfnbp5fbau4slcmyrzm

Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

Arvinder Kaur, Inderpreet Kaur
2014 Lecture Notes on Software Engineering  
Predicting software quality early helps in using testing resources optimally. So, many statistical and machine learning techniques are used to predict quality classes in software.  ...  Producing quality software is a very challenging task looking at the size and complexity of software developed these days.  ...  Machine Learning and Model Prediction Machine Learning is a branch of Artificial Intelligence Classification is data mining technique used to predict group membership for data instances.  ... 
doi:10.7763/lnse.2014.v2.118 fatcat:d6tohjgk2fg2tb23ksadfgbdhu

A Machine Learning Approach for Developing Test Oracles for Testing Scientific Software

Junhua Ding, Dongmei Zhang
2016 Proceedings of the 28th International Conference on Software Engineering and Knowledge Engineering  
This paper introduces a machine learning approach for iteratively developing metamorphic relations.  ...  Absence of test oracles is the grand challenge for testing complex scientific software. Metamorphic testing is the novel technique for developing test oracles on metamorphic relations.  ...  Xin-Hua Hu, Eric King at East Carolina University for assistances of the experiments. This research is supported in part by grant CNS-1262933 and CNS-1560037 from the National Science Foundation.  ... 
doi:10.18293/seke2016-137 dblp:conf/seke/DingZ16 fatcat:4jucbmms6nfrhiputb7dhtkqna

A Model-Driven Approach to Machine Learning and Software Modeling for the IoT [article]

Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann
2021 arXiv   pre-print
For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions  ...  Models are used in both Software Engineering (SE) and Artificial Intelligence (AI).  ...  Acknowledgements This work is partially funded by the German Federal Ministry for Education and Research (BMBF) through the Software Campus initiative (project ML-Quadrat).  ... 
arXiv:2107.02689v3 fatcat:vsbwfcbbs5bqrfyslhb7jlvxoy

Mining assumptions for software components using machine learning

Khouloud Gaaloul, Claudio Menghi, Shiva Nejati, Lionel C. Briand, David Wolfe
2020 Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering  
Software verification approaches aim to check a software component under analysis for all possible environments.  ...  the test generation based on the feedback produced by machine learning.  ...  Our approach combines search-based software testing with machine learning decision trees to learn assumptions.  ... 
doi:10.1145/3368089.3409737 dblp:conf/sigsoft/GaaloulMNBW20 fatcat:aw4ki3odovbtrjmtty3jihexcq
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