Algorithmic Crowdsourcing (AC) is an emerging field in which computational methods are proposed to automate cer- tain aspects of crowdsourcing. A number of AC methods have proposed recently in an attempt to address this problem. However, existing AC approaches are based on highly simplified models of worker behaviour which limit their practical applicability. To make efficient utilization of human resources for crowdsourcing tasks, the following tech- nical challenges remain open: Fairness of the solution, temporal changes in behaviour, optimizing wellbeing, and non-compliance by users. For AI researchers to propose effective solutions to these challenges, labelled datasets reflecting various aspects of human decision-making related to task allocation in crowd-sourcing are needed. We construct an anonymized dataset based on player behavior trajectories captured by a multiagent game platform - Agile Manage. It allows players to demonstrate their task delegation strategies under different scenarios based on key characteristics involved in crowdsourcing task allocation. The game adopts implicit human computation in which players contribute data which are valuable for research through informal games.

Developers' emotional stability in agile software development (ASD) teams is an important factor affecting the success of a project.Traditional Happiness Chart depends on people's self-report and is unable to produce predictive analysis to facilitate decision-making.In this paper, we proposed a Fuzzy Cognitive Map (FCM) based method for analyzing ASD developers' emotional stability.We designed an easy-touse survey questionnaire which can help establish the values of the causal relationship weights to support personalization of the proposed FCM model.The FCM affective model produces two metrics for assessing a given user's emotional stability: 1) the stable state mood value, and 2) the number of iteration steps for the FCM model to reach equilibrium.The two metrics are evaluated through a real-world experiment involving 20 undergraduate students.The results show that they are strong explanation factors for a user's emotional stability with 99% confidence.A regression model based on the user study has also been presented in the paper to help researchers study the relationships between the two metrics and a person's emotional stability computationally in the future.

Task allocation decisions are critical for the success of Agile teams yet not well understood. Traditional survey/interview based methods limit the scale and level of details of data collection. As agile project management (APM) tools are increasingly adopted, they offer a mechanism for unobtrusively collecting behavior data to support research. In this paper, we demonstrate the advantage of APM tool based method of studying task allocation decision-making in agile software engineering compared to survey/interview based methods. Through the proposed HASE online APM tool, we conducted a study involving 20 novice Agile teams consisting of 119 undergraduate software engineering students over an 8 week period. Analysis of the collected data provides insight into the effects of novice Agile team members' competence and the difficulty of the tasks assigned on them on their workload variation, their confidence, and the timeliness of completion of these tasks. These findings can be useful in designing situation-aware task allocation decision support systems to serve Agile teams.

Agile methodologies use user stories to capture software requirements. This often results in team members over emphasizing their understanding of the goals, without proper incorporation of goals from other stakeholders or customers. Existing UML or other goal oriented modeling methods tend to be overly complex for non-technical stakeholders to properly express their goals and communicate them to the agile team. In this paper, we propose a light weight Goal Net based method to model goal requirements in agile software development process to address this problem. It can be used to decompose complex processes into phased goals, and model low level user stories to high level hierarchy goal structures. Our preliminary analysis and studies in educational software engineering contexts show that it can improve agile team's group awareness to project goals and, thus, improve team productivity and artifact quality. The proposed approach was evaluated in university level agile software engineering projects. It has achieved an improvement of over 50 percentage points in terms of the proportion of high quality user stories generated by students compared to the standard user story template used in Scrum,

The philosophy of Agile software development advocates the spirit of open discussion and coordination among team members to adapt to incremental changes encountered during the process. Based on our observations from 20 agile student development teams over an 8-week study in Beihang University, China, we found that the task allocation strategy as a result of following the Agile process heavily depends on the experience of the users, and cannot be guaranteed to result in efficient utilization of team resources. In this research, we propose a context-aware task allocation decision support system that balances the considerations for quality and timeliness to improve the overall utility derived from an agile software development project.We formulate the agile process as a distributed constraint optimization problem, and propose a technology framework that assesses individual developers' situations based on data collected from a Scrum-based agile process, and helps individual developers make situation-aware decisions on which tasks from the backlog to select in real-time. Preliminary analysis and simulation results show that it can achieve close to optimally efficient utilization of the developers' collective capacity. We plan to build the framework into a computer-supported collaborative development platform and refine the method through more realistic projects.