ABSTRAT:

Open source projects rely on collaboration of members from all around the world using web technologies like GitHub and Gerrit. This mixture of people with a wide range of backgrounds including minorities like women, ethnic minorities, and people with disabilities may increase the risk of offensive and destroying behaviours in the community, potentially leading affected project members to leave towards a more welcoming and friendly environment. To counter these effects, open source projects increasingly are turning to codes of conduct, in an attempt to promote their expectations and standards of ethical behaviour. In this first of its kind empirical study of codes of conduct in open source software projects, we investigated the role, scope and influence of codes of conduct through a mixture of quantitative and qualitative analysis, supported by interviews with practitioners. We found that the top codes of conduct are adopted by hundreds to thousands of projects, while all of them share 5 common dimensions

EXISTING SYSTEM :

However, some communities find codes of conduct repressive and a threat for open source communities. One common argument is that participants in open source communities are mature enough to deal with debates and differences, and hence it should be obvious for members how to behave. Some strong opponents explicitly picked a “No Code of Conduct” for their communities4. Furthermore, even in projects that do adopt a code of conduct, the adoption process suscitates substantial discussion because of doubts whether codes of conducts work and disagreement about what should go into them and the exact wording to use5. Since there is no empirical evidence regarding the status, nature of, and procedure for establishing codes of conduct in open source projects, the primary purpose of this paper is to empirically examine codes of conduct in open source projects, identifying the procedures followed in their implementation and monitoring, as well as understanding its scope and impact in open source communities. To this end, we address the following research questions

EXISTING SYSTEM DISADVANTAGES:

1.LESS ACCURACY

2. LOW EFFICIENCY

PROPOSED SYSTEM :

While the above limitations are acceptable to obtain an order of magnitude, our second approach instead uses a second group of data sets and involves manual analysis to get an accurate set of mappings between projects and the codes of conduct that they use. To do this, we used the principles of systematic reviews proposed by Kitchenhand [17]: our population is “Open Source communities”, our intervention is “Code of Conduct”, and two electronic databases are used, i.e. Github9 and Google. The latter allows to find projects not hosted on GitHub or whose code of conduct is not stored in their version control system. We used the same queries for both GitHub and Google: 1) “code of conduct” “open source” and 2) “code of conduct” software community. Quotation marks were explicitly added to reduce the number of false positive hits. Furthermore, we added “open source” and software community to filter out codes of conduct for conferences and other events as opposed to codes of conduct for software projects, which are the focus of this paper

PROPOSED SYSTEM ADVANTAGES:

1.HIGH ACCURACY

2.HIGH EFFICIENCY

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10