1 / 8

Bayesian Networks For Evidence-Based Decision-Making in Soft

Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/ http://inplanttrainingchennai.com/

kaashivit
Download Presentation

Bayesian Networks For Evidence-Based Decision-Making in Soft

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Machine TranslatedEvaluationTechniques in validatingthewebbasedtestingframework IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 40, NO. 6, JUNE 2014 Bayesian Networks For Evidence-Based Decision-Making in Software Engineering”

  2. A Software /Manufacturing Research Company Run By Microsoft Most Valuable Professional VenkatesanPrabu .J MANAGING DIRECTOR Microsoft Web Developer Advisory Council team member and a well known Microsoft Most Valuable Professional (MVP) for the year 2008, 2009, 2010,2011,2012,2013 ,2014. LakshmiNarayanan.J GENERAL MANAGER BlackBerry Server Admin. Oracle 10g SQL Expert. Arunachalam.J Electronic Architect Human Resourse Manager

  3. Abstract • Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). • In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the • parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. • In two industrial cases, we build sample hybrid BNs and evaluate their performance. The results of our empirical analyses show that hybrid BNs are powerful frameworks that combine expert knowledge with quantitative data.

  4. Existing System • In current trends, testing that focuses on web applications called web testing are done before deploying the website in the live. • In the existing system, an approach called static test generation is used that generates a test paper automatically according to the user specification for the purpose of estimation. • Papers are generated based on a high quality multi objective technique. Reliable capability is the main concern that was focused here. • Using the user’s specification, a fine solution called fractional optimal solution is used in order to identify the nature of the specification that are then utilize by the option called 0-1 Integer Linear program technique. • The numerous objective static tests are generated from the database constitute of questions in order to frame test paper for students. The evaluation of the test paper is done by the automatic solution checker.

  5. Proposed System • In the proposed system, the testing such as functional, black box, reliable capacity as well as respective testing automatically are concentrated in the online assessment web page that uses user’s specification. • In our proposed system, The multiple objective static tests are generated from the database constitute of questions in order to frame test paper for students and the evaluation of the test paper is done by the automatic solution manager as in the existing system. • A tool named QTP, Quick Test professional, an automated functional graphical user Interface testing tool that allows the automation of user actions on a web or client based computer application is used to check all the functionality of the assessment webpage. • The process of filter the nodes is done in order to provide an effective branching strategy to reduce branch and bound search tree size, this in turn provide a feasible way to find an optimized solution for the problem of unevaluated nodes.

  6. Architecture Diagram

  7. Records Breaks Asia Book Of Records Tamil Nadu Of Records India Of Records MVP Awards World Record

  8. Services: A Software /Manufacturing Research Company Run By Microsoft Most Valuable Professional Inplant Training. Internship. Workshop’s. Final Year Project’s. Industrial Visit. Contact Us: +91 98406 78906,+91 90037 18877 kaashiv.info@gmail.com www.kaashivinfotech.com Shivanantha Building (Second building to Ayyappan Temple),X41, 5th Floor, 2nd avenue,Anna Nagar,Chennai-40.

More Related