M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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    Similarities in Challenges faced by Developers: Investigations on Stack Overflow and GitHub
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Pandya, Nidhi; Tiwari, Saurabh
    A large amount of rich data available in today�s world encounters a lot of opportunities to analyze the data and identify some useful patterns from them. However, dealing with such data requires automated frameworks and knowledge of programming languages. Java and Python are the most commonly and widely used programming languages among the developers which it is evident from the queries and issues posted on Stack Overflow and GitHub. Despite the popularity of both Java and Python, the challenges in transitioning from one technology to another technology are hard for individuals and industries. In this thesis work, we aim to investigate similarities in the challenges faced by the developers while dealing with both the programming languages. To achieve this goal, we formulated three research questions (RQs) for understanding the topics and issues asked and faced by developers. To achieve the results, we have used the topic modeling technique Latent Dirichlet Allocation (LDA).We have also identified the temporal trend of asking new questions on Stack Overflow for Java and Python programming languages (PLs). Our results revealed the changing trend, in the year 2015 onwards, from Java to Python and inclined towards Python from the number of the new posts in a Stack Overflow. We performed analysis on 18,892 Stack Overflow uestions related to Java and Python PLs and 42,674 issues from 22 different GitHub repositories, 11 for each PL. Our results indicates that questions asked on the Stack Overflow are co-related to issues posted by developers on GitHub during real-time development for a respective PL.
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    Service integration on social network
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Patel, Mehul; Chaudhary, Sanjay; Bise, Minal
    Microblogging services are part of social network platforms, which allow people to exchange short messages. Social networks provide people to play an active role in collecting, analyzing and reporting news and information. People can use social network platform for marketing, buying and selling of their products. A sellers can tweet regarding product information including links of related photos, videos etc. A buyer can show interest in the product by means of tweets. Social network can be used as a mechanism to bring sellers and buyers closer. It provides a common platform for buyers and sellers to sell and buy their products. Microblogs can be parsed and analyzed to generate useful suggestions, e.g. sellers can be informed about potential buyers to get higher profit. Such information can be used to generate classified information to help users to take decision, e.g. minimum expected price of a crop that sellers expect in a given region. Microblogs can be written in different regional languages. Agro-produce marketing information can be processed and then stored in RDF/RDF(S) and OWL data store. SPARQL and conjunctive queries with pellet like reasoner or SPARQL-DL can be used to generate classified summarized information from RDF/RDF(S) and OWL data store.
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    SPARQL query optimization
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Singh, Rohit Kumar; Chaudhary, Sanjay
    Query Optimization is the process of selecting the most efficient query evaluation plan among the many strategies possible for processing a given query, especially if the query is complex. The users are not expected to write their queries in such a way so that they can be processed efficiently; rather it is expected from system to construct a query evaluation plan that minimizes the cost of query evaluation. In any query optimization, the goal is to find the execution plan which is expected to return the result set without actually executing the query or subparts with optimal cost. Query engines for ontological data mostly execute user queries without considering any optimization. Especially for large ontologies,optimization techniques are required to ensure that query results are delivered within reasonable time. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. So, Query optimization may speed up SPARQL query answering by knowledge intensive reformulation. In our research work, we have proposed learning approach to solve this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and properties to construct rules from a ontology with many concepts. The learned semantic rules are effective for optimization of SPARQL query because they match query patterns and reflect data regularities.