The fast, unstructured and turbulent nature of decision making in agile software development (ASD) teams at times necessitates software-based support tools (Dabrowski, Acton, Drury, Conboy, and Dabrowska, 2011). The demands on development teams are increasing in terms of delivery expectations, and therefore requires innovation in how decision support tools are used. Decisions in agile teams are intense, with teams iteratively producing customer-focused software in short bursts of time.
Crucial decisions in ASD relate to managing, planning, execution, reviews and retrospectives (Power and Drury, 2010). However, the data needed for decision-making are often spread across a range of collaboration platforms and/or information sources, for example, instant messaging, email, scanned documentation, spreadsheets, and proprietary document formats associated with various tools. Agile resources and decision tools require the ability to deal with structured and unstructured data and need to be somewhat elastic or scaleable in the context of projects as they arise (Dabrowski et al., 2011). However, some support tools currently used in ASD are low barrier entry solutions such as scrumboards: others are proprietary or open source. Existing tools include the common applications of word processors, spreadsheets, presentation software and instant messaging. Some agile specific software applications in use include Rally, Scrumdo, Jira, Greenhopper, Hansoft and Version One (Sudheer, 2012). Many of these tools are focused on the management of projects, project planning, tracking with some lending themselves as prediction tools to others, for example back log, iteration management tools, and burn-ups (Version One, 2011).
Whilst existing support tools focus on the various phases of software development, they focus on tasks, whereas integrated decision support is the next level of evolution required to ensure shortening the development time and ultimately support more efficient decision making. The basis for the ideal agile tool is to integrate these with tracking, tracing, automated build, data sharing for collaboration and feedback in addition to scrum project management and the decision making embedded. This necessitates the incorporation of people management over distributed teams with changing customer requirements and expectations so as to make software development in agile environments more efficient (Dabrowski et al., 2011). Further, shortening software development time is the ideal (Cooper, Cerulli, Lawson, Peng, and Rezgui, 2005). One way to achieve this would be through an embedded expert domain knowledge based system that could work through the potential development decisions that are relatively common and routine and time consuming in development projects (Pearlson and Saunders, 2009). Such decision support for ASD would take a snapshot of the customer requirements and present a snapshot prototype development project, to help focus on the higher impacting decision points rather than routine or low-impact decisions. In particular, potential positive impacts may include time to task completion, reduced bug count, reduction in task dependencies, increase in data availability, and product owner confidence (Minkiewicz, 2010). Further, such decision support aids may facilitate an increased innovative capacity in the team, and by extension, the software development organisation, through the provision of competitive advantage.
The study identifies, reviews and collates the functional elements of current decision support software being used in ASD, and identifies best practice and the requirements of agile team decision makers. The study involves key partnerships and industry contributors, including business technology consultancies, middle and back office solutions companies, enterprise security software solutions providers with input from a continuously evolving large global processor technology company. The study identifies which software tools are being used to support effective decision making throughout the agile process with the central objective of providing an innovative targeted business intelligence solution for optimal decision making in managing ASD.