Middleware 2013 - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Middleware 2013 write by David Eyers. This book was released on 2013-12-04. Middleware 2013 available in PDF, EPUB and Kindle. This book constitutes the refereed proceedings of the ACM/IFIP/USENIX 14th International Middleware Conference, held in Beijing, China, in December 2013. The 24 revised full papers presented were carefully reviewed and selected from 189 submissions. The papers cover a wide range of topics including design, implementation, deployment and evaluation of middleware for next-generation platforms such as cloud computing, social networks and large-scale storage and distributed systems. The middleware solutions introduced provide features such as availability, efficiency, scalability, fault-tolerance, trustworthy operation and support security and privacy needs.
Agile Processes in Software Engineering and Extreme Programming
Agile Processes in Software Engineering and Extreme Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Agile Processes in Software Engineering and Extreme Programming write by Casper Lassenius. This book was released on 2015-05-15. Agile Processes in Software Engineering and Extreme Programming available in PDF, EPUB and Kindle. This book contains the refereed proceedings of the 16th International Conference on Agile Software Development, XP 2015, held in Helsinki, Finland, in May 2015. While agile development has already become mainstream in industry, this field is still constantly evolving and continues to spur an enormous interest both in industry and academia. The XP conference series has always played, and continues to play, an important role in connecting the academic and practitioner communities, providing a forum for both formal and informal sharing and development of ideas, experiences, and opinions. The theme of XP 2015 "Delivering Value: Moving from Cyclic to Continuous Value Delivery" reflects the modern trend towards organizations that are simultaneously very efficient and flexible in software development and delivery. The 15 full and 7 short papers accepted for XP 2015 were selected from 44 submissions. All of the submitted papers went through a rigorous peer-review process. Additionally, 11 experience reports were selected from 45 proposals, and in each case the authors were shepherded by an experienced researcher.
Software Engineering Trends and Techniques in Intelligent Systems
Software Engineering Trends and Techniques in Intelligent Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Software Engineering Trends and Techniques in Intelligent Systems write by Radek Silhavy. This book was released on 2017-04-07. Software Engineering Trends and Techniques in Intelligent Systems available in PDF, EPUB and Kindle. This book presents new approaches and methods to solve real-world problems as well as exploratory research describing novel approaches in the field of software engineering and intelligent systems. It particularly focuses on modern trends in selected fields of interest, introducing new algorithms, methods and application of intelligent systems in software engineering. The book constitutes the refereed proceedings of the Software Engineering Trends and Techniques in Intelligent Systems Section of the 6th Computer Science On-line Conference 2017 (CSOC 2017), held in April 2017.
Middleware Solutions for Wireless Internet of Things
Middleware Solutions for Wireless Internet of Things - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Middleware Solutions for Wireless Internet of Things write by Paolo Bellavista. This book was released on 2019-07-15. Middleware Solutions for Wireless Internet of Things available in PDF, EPUB and Kindle. The proliferation of powerful but cheap devices, together with the availability of a plethora of wireless technologies, has pushed for the spread of the Wireless Internet of Things (WIoT), which is typically much more heterogeneous, dynamic, and general-purpose if compared with the traditional IoT. The WIoT is characterized by the dynamic interaction of traditional infrastructure-side devices, e.g., sensors and actuators, provided by municipalities in Smart City infrastructures, and other portable and more opportunistic ones, such as mobile smartphones, opportunistically integrated to dynamically extend and enhance the WIoT environment. A key enabler of this vision is the advancement of software and middleware technologies in various mobile-related sectors, ranging from the effective synergic management of wireless communications to mobility/adaptivity support in operating systems and differentiated integration and management of devices with heterogeneous capabilities in middleware, from horizontal support to crowdsourcing in different application domains to dynamic offloading to cloud resources, only to mention a few. The book presents state-of-the-art contributions in the articulated WIoT area by providing novel insights about the development and adoption of middleware solutions to enable the WIoT vision in a wide spectrum of heterogeneous scenarios, ranging from industrial environments to educational devices. The presented solutions provide readers with differentiated point of views, by demonstrating how the WIoT vision can be applied to several aspects of our daily life in a pervasive manner.
Machine Learning-Based Bug Handling in Large-Scale Software Development
Machine Learning-Based Bug Handling in Large-Scale Software Development - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning-Based Bug Handling in Large-Scale Software Development write by Leif Jonsson. This book was released on 2018-05-17. Machine Learning-Based Bug Handling in Large-Scale Software Development available in PDF, EPUB and Kindle. This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations. The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.
