His main research concerns Software, Software engineering, Data mining, Software quality and World Wide Web. His Software research incorporates themes from Code and Source code. Ahmed E. Hassan has researched Software engineering in several fields, including Software as a service, Software maintenance, Software development, KPI-driven code analysis and Software peer review.
His Data mining research includes elements of Predictive modelling, Machine learning, Random forest and Artificial intelligence. His study in Software quality is interdisciplinary in nature, drawing from both Empirical research and Software construction. His World Wide Web research integrates issues from Android and Internet privacy.
Software, Software engineering, World Wide Web, Software system and Source code are his primary areas of study. Ahmed E. Hassan has included themes like Data science, Data mining, Database and Code in his Software study. The study incorporates disciplines such as Software quality, Software maintenance, Software development, KPI-driven code analysis and Software peer review in addition to Software engineering.
His study focuses on the intersection of World Wide Web and fields such as Empirical research with connections in the field of Software development process. His work carried out in the field of Software system brings together such families of science as Reliability engineering and Real-time computing. Ahmed E. Hassan interconnects Topic model and Software build in the investigation of issues within Source code.
Ahmed E. Hassan spends much of his time researching Software, World Wide Web, Empirical research, Artificial intelligence and Mobile apps. His Software study combines topics in areas such as Software engineering, Information retrieval and Source code. In general World Wide Web, his work in Download is often linked to Publishing linking many areas of study.
His research integrates issues of License and Internet privacy in his study of Empirical research. His study on Artificial intelligence also encompasses disciplines like
His primary areas of study are Software, World Wide Web, Data mining, Artificial intelligence and Software bug. His study on Software analytics is often connected to Experience report as part of broader study in Software. His studies deal with areas such as User interface, Android and Central processing unit as well as World Wide Web.
His Data mining research incorporates elements of Data modeling, Categorical variable and Resolution. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition. His Software bug study also includes fields such as
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Predicting faults using the complexity of code changes
Ahmed E. Hassan.
international conference on software engineering (2009)
Studying the effectiveness of application performance management (APM) tools for detecting performance regressions for web applications: an experience report
Tarek M. Ahmed;Cor-Paul Bezemer;Tse-Hsun Chen;Ahmed E. Hassan.
mining software repositories (2016)
What are developers talking about? An analysis of topics and trends in Stack Overflow
Anton Barua;Stephen W. Thomas;Ahmed E. Hassan.
Empirical Software Engineering (2014)
The road ahead for Mining Software Repositories
A.E. Hassan.
2008 Frontiers of Software Maintenance (2008)
Predicting change propagation in software systems
A.E. Hassan;R.C. Holt.
international conference on software maintenance (2004)
A large-scale empirical study of just-in-time quality assurance
Y. Kamei;E. Shihab;B. Adams;A. E. Hassan.
IEEE Transactions on Software Engineering (2013)
Clustering WSDL Documents to Bootstrap the Discovery of Web Services
Khalid Elgazzar;Ahmed E. Hassan;Patrick Martin.
international conference on web services (2010)
What Do Mobile App Users Complain About
Hammad Khalid;Emad Shihab;Meiyappan Nagappan;Ahmed E. Hassan.
IEEE Software (2015)
System and method for controlling device usage
Daryl J. Martin;Ahmed E. Hassan;John F. (Sean) Wilson.
(2006)
Beyond DCG: user behavior as a predictor of a successful search
Ahmed Hassan;Rosie Jones;Kristina Lisa Klinkner.
web search and data mining (2010)
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