Evolution of developer collaboration on the Jazz platform: A study of a large scale agile project

Collaboration is a key aspect of the agile philosophy of software development. As a software system matures over iterations, trends of developer collaboration can offer valuable insights into project dynamics. In this paper, we study evolution of developer collaboration for a large scale agile project on the Jazz platform. We construct networks of collaboration based on developer affiliations across comments on work items and file changes; and then compare parameters of such networks with established results from networks of scientific collaborations. The comparisons illuminate interesting facets of developer collaboration on the Jazz platform. Such perception helps deeper understanding of the role of interaction in agile projects, as well as more effective project governance.</p

Coping with distance: An empirical study of communication on the Jazz platform

Global software development - which is characterized by teams separated by physical distance and/or time-zone differences - has traditionally posed significant communication challenges. Often these have caused delays in completing tasks, or created misalignment across sites leading to re-work. In recent years, however, a new breed of development environments with rich collaboration features have emerged to facilitate cross-site work in distributed projects. In this paper we revisit the question "does distance matter?" in the context of IBM Jazz Platform - a state-of-the-art collaborative development environment. We study the ecosystem of a large distributed team of around 300 members across 35 physical locations, which uses the Jazz platform for agile development. Our results indicate that while there is a delay in communication due to geographic separation, teams try to reduce the impact of delays by having a large percentage of work distributed within same/few time zones and working beyond regular office hours to interact with distributed teams. We observe different communication patterns depending on the roles of the team members, with component leads and project managers having a significantly higher overhead than development team members. We discuss the practical implications of our findings in terms of some best practices that can help lessen the impact of distance.</p

Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning

Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of the prediction being conformant or meeting the goal. Hence, we propose a goal-oriented next best activity recommendation. Our proposed framework uses a deep learning model to predict the next best activity and an estimated value of a goal given the activity. A reinforcement learning method explores the sequence of activities based on the estimates likely to meet one or more goals. We further address a real-world problem of multiple goals by introducing an additional reward function to balance the outcome of a recommended activity and satisfy the goal. We demonstrate the effectiveness of the proposed method on four real-world datasets with different characteristics. The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques