Journal of Software Engineering & Software Testing (ISSN: 2457-0516)

Journal of Software Engineering & Software Testing

The Journal of Software engineering & Software Testing deals with the application of engineering to software that includes documenting the requirements of the software through application of basic design principles. The journal also emplasizes to analyze and design alternatives keeping in mind the utility of the finished product and whether the final product meets its requirements. At the same The journal also emphasizes on the safety, reliability, cost-affectivity and functional aspects of the software.

Some of the topics covered under this journal (but not limited to them) are:

    • Mathematics
    • Chemistry
    • Basic Engineering
    • Computer Literacy
    • Physics Laboratory
    • Engineering Graphics
    • Biology for Engineering
    • Principles of Environmental Science
    • Material Science
    • Digital Computer Fundamentals
    • Computer Organization and Architecture
    • Data Structures and Algorithms
    • Software Engineering Principles
    • Object Oriented Programming
    • Microprocessors
    • Software Design
    • Computer Skills
    • Discrete Mathematics
    • Computer Networks
    • Industrial management and economics
    • Software quality management
    • Web technology

Vol 1, No 1 (2019): Truth Discovery in Big Data Social Media Application

Authors:-M. Nigade, M. Raut, P. Mane, S. Phadatare

Abstract:-In this system first one is “misinformation spread” where a significant number of sources are contributing to false claims, making the identification of truthful claims difficult. For example, on, Instagram, rumors, Twitter scams, and influence bots are common examples of sources colluding, either intentionally or unintentionally, to spread misinformation and obscure the truth. The challenge is “data sparsity” or the “long-tail phenomenon” where a majority of sources only contribute a small number of claims, providing insufficient evidence to determine those sources’ trustworthiness. For example, in the Twitter datasets that we collected during real-world events, more than 90only contributed to a single claim. Third, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. We are going develop a Scalable and Robust Truth Discovery (SRTD) scheme to address the above all challenges. In this, the SRTD scheme jointly quantifies both the reliability of sources and the credibility of claims using a principled approach.

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