Elmar Juergens

Florian Deissenboeck, Stefan Wagner, Markus Pizka et al.

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.

Jakob Rott, Rainer Niedermayr, Elmar Juergens et al.

There is no metric that determines how well the implementation of a ticket has been tested. As a consequence, code changed within the context of a ticket might unintentionally remain untested and get into production. This is a major problem, because changed code is more fault-prone than unchanged code. In this paper, we introduce the metric ticket coverage which puts test coverage into the context of tickets. For each ticket, it determines the ratio of changed methods covered by automated or manual tests. We conducted an empirical study on an industrial system consisting of 650k lines of Java code and show that ticket coverage brings transparency into the test state of tickets and reveals relevant test gaps.

Elmar Juergens, Florian Deissenboeck, Martin Feilkas et al.

Due to their pivotal role in software engineering, considerable effort is spent on the quality assurance of software requirements specifications. As they are mainly described in natural language, relatively few means of automated quality assessment exist. However, we found that clone detection, a technique widely applied to source code, is promising to assess one important quality aspect in an automated way, namely redundancy that stems from copy&paste operations. This paper describes a large-scale case study that applied clone detection to 28 requirements specifications with a total of 8,667 pages. We report on the amount of redundancy found in real-world specifications, discuss its nature as well as its consequences and evaluate in how far existing code clone detection approaches can be applied to assess the quality of requirements specifications in practice.