Requirements change and so (should) do requirements artifacts, such as use cases. However, we have little knowledge about which changes requirements engineers actually perform on use cases. We do not know what is changing, at which locations use cases change and need a deeper understanding of which changes are problematic in terms of difficult or risky.

Maintenance of unused features leads to unnecessary costs. Therefore, identifying unused features can help product owners to prioritize maintenance efforts. We present a tool that employs dynamic analyses and text mining techniques to identify use case documents describing unused features to approximate unnecessary features. We report on a preliminary study of an industrial business information system over the course of one year quantifying unused features and measuring the performance of the approach. It indicates the relevance of the problem and the capability of the presented approach to detect unused features.

To execute system tests, two fundamentally different execution techniques exist: manual and automated execution. For each system test suite, one must decide how to employ those techniques (this strategy is called execution mode). Despite general conditions such as fixed testing strategies or development philosophies, almost all projects permit a wide range of possible execution modes to choose from. In industry, execution techniques are often chosen by experts based on rules of thumb, experience and best practices. Although the results are mostly tolerable, they may be not cost-effective. In retrospect, it is often unclear on what basis those decisions were made, making it difficult to assess whether they are still valid. Finally, it is hard to predict the costs for test execution beforehand. We introduce a cost model to estimate the economic impact of execution modes. Our cost model is based on expert estimations and gives additional input for testing experts in balancing pros and cons of execution modes at hand. Furthermore, it helps documenting and persists decisions during the life time of a test suite. Additionally, we report on a first case study, applying our cost model in industry.

As a measure of software quality, current static code analyses reveal thousands of quality defects on systems in brown-field development in practice. Currently, there exists no way to prioritize among a large number of quality defects and developers lack a structured approach to address the load of refactoring. Consequently, although static analyses are often used, they do not lead to actual quality improvement. Our approach recommends to remove quality defects, exemplary code clones and long methods, which are easy to refactor and, thus, provides developers a first starting point for quality improvement. With an empirical industrial Java case study, we evaluate the usefulness of the recommendation based on developers’ feedback. We further quantify which external factors influence the process of quality defect removal in industry software development.

A system’s requirements and its architecture are usually developed at least partly in parallel. This demands a continuous and automated assessment to confirm that the architecture conforms to its requirements. To enable such an assessment, the stepwise formalization of informal requirements has been proposed. However, there is no canonical set of artifacts and analysis techniques that has been evaluated for this task in practice yet. In this paper we propose an artifact model and a process that enables the continuous conformance assessment between requirements and architecture in a model-based context. We evaluate both in a development project with a group of students.

Tests are central artifacts of software systems and play a crucial role for software quality. In system testing, a lot of test execution is performed manually using tests in natural language. However, those test cases are often poorly written without best practices in mind. This leads to tests which are not maintainable, hard to understand and inefficient to execute. For source code and unit tests, so called code smells and test smells have been established as indicators to identify poorly written code. We apply the idea of smells to natural language tests by defining a set of common Natural Language Test Smells (NLTS). Furthermore, we report on an empirical study analyzing the extent in more than 2800 tests of seven industrial test suites.

Software systems contain unnecessary code. Its maintenance causes unnecessary costs. We present tool-support that employs dynamic analysis of deployed software to detect unused code as an approximation of unnecessary code, and static analysis to reveal its changes during maintenance. We present a case study on maintenance of unused code in an industrial software system over the course of two years. It quantifies the amount of code that is unused, the amount of maintenance activity that went into it and makes the potential benefit of tool support explicit, which informs maintainers that are about to modify unused code.

Tests are central artifacts of software systems. Therefore, understanding tests is essential for activities such as maintenance, test automation, and efficient execution. Redundancies in tests may significantly decrease their understandability. Clone detection is a technique to find similar parts in software artifacts. We suggest using this technique to gain a better understanding of tests and to provide guidance for testing activities. We show the capabilities as well as the limits of this approach by conducting a case study analyzing more than 4000 tests of seven industrial software systems.