a research program

Most process research relies heavily on the use of terms and concepts whose validity depends on a variety of assumptions to be met. As it is difficult to guarantee that they are met, such work continually runs the risk of being invalid. We propose a different and complementary approach to understanding process: Perform all description bottom-up and based on hard data alone. We call the approach actual process and the data actual events. Actual events can be measured automatically. This paper describes what has been done in this area already and what are the core problems to be solved in the future

Analytics for software development.

ABSTRACT Despite large volumes of data and many types of metrics, software projects continue to be difficult to predict and risky to conduct. In this paper we propose software analytics which holds out the promise of helping the managers of software projects turn their plentiful information resources, produced readily by current tools, into insights they can act on. We discuss how analytics works, why it's a good fit for software engineering, and the research problems that must be overcome in order to realize its promise

Tool support for continuous quality controlling

Over time, software systems suffer gradual quality decay and therefore costs can rise if organizations fail to take proactive countermeasures. Quality control is the first step to avoiding this cost trap. Continuous quality assessments help users identify quality problems early, when their removal is still inexpensive; they also aid decision making by providing an integrated view of a software system's current status. As a side effect, continuous and timely feedback helps developers and maintenance personnel improve their skills and thereby decreases the likelihood of future quality defects. To make regular quality control feasible, it must be highly automated, and assessment results must be presented in an aggregated manner to avoid overwhelming users with data. This article offers an overview of tools that aim to address these issues. The authors also discuss their own flexible, open-source toolkit, which supports the creation of dashboards for quality control

Framework for a service-oriented measurement infrastructure

Magdeburg, Univ., Fak. für Informatik, Diss., 2009Martin Kun