Gurdeep Virdi

Crowd Build: A Methodology for Enterprise Software Development Using Crowdsourcing

We present and evaluate a software development methodology that addresses key challenges for the application of Crowd sourcing to an enterprise application development. Our methodology presents a mechanism to systematically break the overall business application into small tasks such that the tasks can be completed independently and in parallel by the crowd. Our methodology supports automated testing and automatic integration. We evaluate our methodology by developing a web application through Crowd sourcing. The methodology was tested through two Crowd sourcing models: one through contests and the other through hiring freelancers. We present various metrics of the Crowd sourcing experiment and compare against the estimate for the traditional software development methodology.

A framework to preserve confidentiality in crowdsourced software development

We propose a framework to preserve confidential information in a crowdsourced software development. The software industry is moving towards gig economy where majority of workforce is freelancers. The freelancers may have varying level of trust. Hence, protection of confidential information is becoming an increasingly important subject. In this paper, we discuss various challenges in protecting sensitive information in software development projects and propose a confidentiality preserving software development process. We perform a preliminary evaluation of the process. We use an information theoretic approach to protect confidential information. Results demonstrate the feasibility of the framework and uncovers several aspects that requires further research studies.

Towards Adopting Alternative Workforce for Software Engineering

This paper proposes an approach for adopting alternative workforce in an organization. Alternative workforce refers to a pool of workers who work for the organization as contract workers or as crowd workers for a set of specific tasks or duration. Adoption of crowd workers as an alternative workforce is gaining a lot of attention these days. However, it is still not widely adopted by big organizations because of the concerns related to quality, timeliness, and confidentiality. A partial adoption of crowd workforce is a natural next step to leverage the benefits of crowdsourcing. The above partial adoption creates a hybrid workforce structure where different type of workers, such as full time employees, contractors, and crowd workers, work for the organization. A number of challenges need to be addressed for the above model to succeed. For instance, hiring right workers, establishing a proper collaboration among the workers distributed across geographies, and assessing the workers for confidentiality and privacy. This paper proposes a platform that alleviates some of the above challenges. We present a pilot performed on the platform and initial experiences gained from the adoption of the platform.

Analyzing on-boarding time in context of crowdsourcing

Crowdsourcing is an emerging area which leverages collective intelligence of the crowd. Although crowdsourcing provides several benefits, it also brings uncertainty in any project execution. The uncertainty may be because of the time taken in on-boarding workers and lack of confidence in workers. The On-boarding time specifically becomes important when tasks are of short duration as it is not worth spending too much of time in on-boarding a worker for short task. In this paper, we empirically analyze 59,597 tasks data from Upwork, an online marketplace, to understand major factors that impact On-boarding time. We identified that certain factors, such as Feedback, Hiring rate, Total hours spent, Length of requirement etc., affect the On-boarding time. We applied two predictive models to predict the On-boarding time. Our study provides insights for researchers, organizations, etc. who are looking to accomplish their tasks through crowdsourcing and helps them to better understand factors which influence the On-boarding time.