Alpana Dubey

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.

Predicting budget for Crowdsourced and Freelance Software development Projects

In this paper we propose an approach to estimate the right budget for a crowdsourced or freelance project job. Estimating right budget for a job is an important and challenging exercise. The budget depends on marketplace dynamics; such as job complexity, skills required, duration, etc. As manually understanding the dynamics of marketplace is time consuming and error prone, we propose a data driven approach to estimate the right budget. We study various factors that contribute to final remuneration made upon job completion and use the above studies to recommend an approach. The approach is validated on the data collected from Upworks database, which contains data from the job activity happening on the largest freelancing website. Our results indicate that budget for a job can be relatively well predicted as compared to baseline approach.

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 Gender Pay Gap in Freelancing Marketplace

Diversity and inclusion are becoming major focus areas for most of the organizations these days. It has shown to bring several positive impacts to organizations such as highly engaged and motivated employees, improved team dynamics, sustainable team structure, and better work outcome. Pay and career opportunity are some of the indicators to assess diversity and inclusion practice. In this paper, we study gender pay gap in freelancing marketplace. Freelancing marketplaces are open to everyone and gender neutral; hence, it is expected that gender pay gap should not be much over these platforms as freelancers can quote or negotiate the price for the jobs as per their wish. However, our study, performed on 37,599 freelancers, reveals a gap in pay between male and female freelancers. Moreover, the study shows that female freelancers undervalue themselves compared to male freelancers having similar profile. Our study suggests that there is a need to address this large scale pay gap issue by guidance and counseling of female freelancers.

An Approach for Collaborative Quality Assessment of Software

Software quality assessment is an important activity done during software development. Quality assessment varies based on needs. For example, security focused quality assessment looks for security specific issues and performance focused assessment looks for performance issues in software. The above focus is mainly driven by the type of software being developed. Moreover, metrics for measuring software quality may vary from team to team. This makes the reuse of assessment models very challenging. Especially when metrics are defined based on the warning data, such as warnings, coming from different types of warning data sources and there is no uniform nomenclature and classification of warning data. In this paper, we propose a collaborative quality assessment approach. The approach is based on users contributing to software quality assessment models on a centralized quality assessment platform which is available to other users for customization and reuse. The proposed approach helps in creating a reusable knowledgebase of assessment models.

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.