際際滷shows by User: alaahamouda79 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: alaahamouda79 / Wed, 20 Jan 2016 06:41:12 GMT 際際滷Share feed for 際際滷shows by User: alaahamouda79 Machine Learning in Software Engineering /slideshow/machine-learning-in-software-engineering-57262213/57262213 mlnadswe-160120064112
Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance. ]]>
Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance. ]]> Wed, 20 Jan 2016 06:41:12 GMT /slideshow/machine-learning-in-software-engineering-57262213/57262213 alaahamouda79@slideshare.net(alaahamouda79) Machine Learning in Software Engineering alaahamouda79 Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mlnadswe-160120064112-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements analysis, design, implementation, testing and maintenance.
Machine Learning in Software Engineering from Alaa Hamouda
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]]> Thu, 16 May 2013 07:56:55 GMT /slideshow/collage-21258061/21258061 alaahamouda79@slideshare.net(alaahamouda79) Collage alaahamouda79 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/collage-130516075655-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Collage from Alaa Hamouda
]]> 294 4 https://cdn.slidesharecdn.com/ss_thumbnails/collage-130516075655-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-alaahamouda79-48x48.jpg?cb=1523522317 Alaa Hamouda has more than 15 years of experience in business and information consultancy, software project management, software quality models of Capability Maturity Models Integration (CMMI), and software development. He provided business and IT consultancy in various organizations and enterprises in the Arabic region. He has played the roles of Software Board member and Engineering Process Group (EPG) member as well as Project Management Office Director (PMO Director) in Egyptian companies. He participated in different types of software development projects. He also participated in establishing reliable standard work procedures in software houses like applying standard project manageme... https://cdn.slidesharecdn.com/ss_thumbnails/mlnadswe-160120064112-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-learning-in-software-engineering-57262213/57262213 Machine Learning in So... https://cdn.slidesharecdn.com/ss_thumbnails/collage-130516075655-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/collage-21258061/21258061 Collage