Search

In the field of improving software processes, one of the clear trends has been the ever tighter engagement of end users in the software development process. This is demonstrated by the shift from Agile processes to Continuous Deployment, which requires more rapid ways to validate the developed software and its value than is possible with traditional communication mechanisms and methods, such as face to face conversations with customers. While post-deployment data has been used for years as an extra data source – companies like Microsoft and Intuit have moved a few steps further from that already – we believe that there are numerous uncovered ways of taking advantage of post-deployment data in software development. In this paper, we study how automatically collected post-deployment data could be used for responding to knowledge needs of software development teams. The paper builds on data collected from a number of companies operating in Finland using a questionnaire study. The focus of questionnaire study was to approach post-deployment data – especially usage data – as means of getting information to support understanding of customer and end users.

Software development methods are shifting towards faster deployments and closer to the end users. Their ever tighter engagement of end-users also requires new technologies for gathering feedback from those users. At the same time, widespread Internet connectivity of different application environments is enabling the collection of this post-deployment data also from sources other than traditional web and mobile software. However, the sheer number of different alternatives of collecting technologies makes the selection a complicated process in itself. In this paper, we describe the process of data-driven software development and study the challenges organizations face when they want to start guiding their development towards it. From these challenges, we extract evaluation criteria for technological approaches to usage data collecting. We list such approaches and evaluate them using the extracted criteria. Using a design science approach, we refine the evaluation criteria to a selection framework that can help practitioners in finding a suitable technological approach for automated collecting of usage data. publishedVersion

Software process data is available in several tools such as version control systems, issue trackers and test and build systems to name a few. Using the data gathered in these software engineering tools would be ideal for collecting different kinds of software processes and product metrics as the data is already automatically gathered by the tools. However, the tools present and store the data in various formats. The data collection methods and interfaces also vary between the tools. This closes the software engineering data into silos and makes it hard to build reusable analysis and visualizations for the data. In this position paper we present a unified model for software engineering data and a framework for data collection, conversion and storing that utilizes our model. The aim of the model is to define a common format for software engineering data which is not dependent on specific software engineering tools or the software engineering process and thus can be used as a basis for building reusable visualization and analysis components. To demonstrate that we can build reusable visualization plugins on top of the framework, we created a timeline visualization plugin. The visualization plugin is used to visualize two data sets from industrial software projects that have different contexts and semantics.