Open-source software abbreviated as OSS is computer software that is available with source code and is provided under a software license that permits users to study, change, and improve the software. For the commercial software the source code and certain other rights are normally reserved for copyright holders,i.e. the company who developes the software. A group of people in a collaborative manner often developes the Open source software, not under the roof of a large organization. This strategy makes open source software cheap, reliable and modifiable if needed.In this context we shall discuss mainly the features of Open Source Software, Existing Open Source Software development models and our proposed model named open incremental model.

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Open-source software abbreviated as OSS is computer software that is available with source code and is provided under a software license that permits users to study, change, and improve the software. For the commercial software the source code and certain other rights are normally reserved for copyright holders,i.e. the company who developes the software. A group of people in a collaborative manner often developes the Open source software, not under the roof of a large organization. This strategy makes open source software cheap, reliable and modifiable if needed. In this context we shall discuss mainly the features of Open Source Software, differences of open source and free software and open source software movement in Indian perspective.

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is widely used to prevent malicious automated attacks on various online services. Text- and image-CAPTCHAs have shown broader acceptability due to usability and security factors. However, recent progress in deep learning implies that text-CAPTCHAs can easily be exposed to various fraudulent attacks. Thus, image-CAPTCHAs are getting research attention to enhance usability and security. In this work, the Neural Style Transfer (NST) is adapted for designing an image-CAPTCHA algorithm to enhance security while maintaining human performance. In NST-rendered image-CAPTCHAs, existing methods inquire a user to identify or localize the salient object (e.g., content) which is solvable effortlessly by off-the-shelf intelligent tools. Contrarily, we propose a Style Matching CAPTCHA (SMC) that asks a user to select the style image which is applied in the NST method. A user can solve a random SMC challenge by understanding the semantic correlation between the content and style output as a cue. The performance in solving SMC is evaluated based on the 1368 responses collected from 152 participants through a web-application. The average solving accuracy in three sessions is 95.61%; and the average response time for each challenge per user is 6.52 seconds, respectively. Likewise, a Smartphone Application (SMCApp) is devised using the proposed method. The average solving accuracy through SMC-App is 96.33%, and the average solving time is 5.13 seconds. To evaluate the vulnerability of SMC, deep learning-based attack schemes using Convolutional Neural Networks (CNN), such as ResNet-50 and Inception-v3 are simulated. The average accuracy of attacks considering various studies on SMC using ResNet-50 and Inception-v3 is 37%, which is improved over existing methods. Moreover, in-depth security analysis, experimental insights, and comparative studies imply the suitability of the proposed SMC.