Abstract-The use of social media especially community Q&A Sites by software development community has been increased significantly in past few years. The ever mounting data on these Q&A Sites has open up new horizons for research in multiple dimensions. Stackoverflow is repository of large amount of data related to software engineering. Software architecture and technology selection verdicts in SE have enormous and ultimate influence on overall properties and performance of software system, and pose risks to change if once implemented. Most of the risks in Software Engineering projects are directly or indirectly coupled with Architectural and Technology decisions (ATD). Advance Architectural knowledge availability and its utilization are crucial for decision making. Existing architecture and technology knowledge management approaches using software repositories give a rich insight to support architects by offering a wide spectrum of architecture and technology verdicts. However, they are mostly insourced and still depend on manual generation and maintenance of the architectural knowledge. This paper compares various software development approaches and suggests crowdsourcing as knowledge ripped approach and brings into use the most popular online software development community/Crowdsourced (StackOverflow) as a rich source of knowledge for technology decisions to support architecture knowledge management with a more reliable method of data mining for knowledge capturing. This is an exploratory study that follows a qualitative and qualitative e-content analysis approach. Our proposed framework finds relationships among technology and architecture related posts in this community to identify architecture-relevant and technology-related knowledge through explicit and implicit knowledge mining, and performs classification and clustering for the purpose of knowledge structuring for future work.

Recent years witness the significant surge in awareness and exploitation of social media especially community Question and Answer (Q&A) websites by academicians and professionals. These sites are, large repositories of vast data, pawing ways to new avenues for research through applications of data mining and data analysis by investigation of trending topics and the topics of most attention of users. Educational Data Mining (EDM) techniques can be used to unveil potential of Community Q&A websites. Conventional Educational Data Mining approaches are concerned with generation of data through systematic ways and mined it for knowledge discovery to improve educational processes. This paper gives a novel idea to explore already generated data through millions of users having variety of expertise in their particular domains across a common platform like StackOverFlow (SO), a community Q&A website where users post questions and receive answers about particular problems. This study presents an EDM framework to classify community data into Software Engineering subjects. The framework classifies the SO posts according to the academic courses along with their best solutions to accommodate learners. Moreover, it gives teachers, instructors, educators and other EDM stakeholders an insight to pay more attention and focus on commonly occurring subject related problems and to design and manage of their courses delivery and teaching accordingly. The data mining framework performs preprocessing of data using NLP techniques and apply machine learning algorithms to classify data. Amongst all, SVM gives better performs with 72.06% accuracy. Evaluation measures like precision, recall and F-1 score also used to evaluate the best performing classifier.

Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions.The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning-based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K-Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni-gram feature set provides best classification results and attains 85.80% accuracy.For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni-gram feature set proved to be the most suitable. K E Y W O R D Sarchitectural knowledge management, stack overflow, crowd-sourced communities, text mining, classification INTRODUCTIONSoftware architectural design appears to have a significant importance in Software Development Life Cycle (SDLC). Its manifests are the early design decisions which help to determine the system development, deployment, and evaluation. To develop a quality project, better architectural decisions are essential and these decisions pose vast challenges for software engineers. Architectural Knowledge Management (AKM) is used to capture

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The idea of a Smart City features the need to upgrade quality, interconnection and execution of different urban administrations with the utilization of data and correspondence advances (ICT). Smart City advances cloudbased and Internet of Things (IoT) based administrations in which certifiable user interface utilize PDAs, sensors and RFIDs. Distributed computing and IoT are by and by two most essential ICT models that are forming the up and coming age of registering. Cloud computing speaks to the new technique for conveying equipment and programming assets to the clients, Internet of Things (IoT) is at present a standout amongst the most well-known ICT ideal models. In the meantime, the IoT idea imagines another age of gadgets (sensors, both virtual and physical) that are associated with the Internet and give diverse administrations to esteem included applications. Focus of this study attention on the integration of Cloud, IoT and IoE technologies for smart city services as well as a review has been made so that we can develop a better smart city that will utilize IoT, IoE in order to provide a better platform for smart city. This paper tends to the joined area of cloud computing, IoT and IoE for any smart city application organization.