Software Project Control Center

Director:Philippsen, M.
Period:November 1, 2009 - December 31, 2015
Coworker:Adersberger, J.; Tausch, N.
Description:

Prototypical implementation of a new tool for quality assurance during software development
Modern software systems are growing increasingly complex with respect to functional, technical and organizational aspects. Thus, both the number of requirements per system and the degree of their interconnectivity constantly increase. Furthermore the technical parameters, e.g., for distribution and reliability are getting more complex and software is developed by teams that are not only spread around the globe but also suffer from increasing time pressure. Due to this, the functional, technical, and organizational control of software development projects is getting more difficult.
The "Software Project Control Center" is a tool that helps the project leader, the software architect, the requirements engineer, or the head of development. Its purpose is to make all aspects of the development process transparent and thus to allow for better project control. To achieve transparence, the tool distills and gathers properties from all artifacts and correlations between them. It presents/visualizes this information in a way suitable for the individual needs of the users.
The Software Project Control Center unifies the access to relations between artifacts (traceability) and to their properties (metrics) within software development projects. Thus, their efficiency can be significantly increased. The artifacts, their relations, and related metrics are gathered and integrated in a central data store. This data can be analyzed and visualized, metrics can be computed, and rules can be checked.
For the Software Project Control Center project we cooperate with the QAware GmbH, Munich. The AIF ZIM program of the German Federal Ministry of Economics and Technology funded the first 30 months of the project.
The Software Project Control Center is divided into two subsystems. The integration pipeline gathers traceability data and metrics from a variety of software engineering tools. The analysis core allows to analyze the integrated data in a holistic way. Each subsystem is developed in a separate subproject.
The project partner QAware GmbH implemented the integration pipeline. The first step was to define TraceML, a modeling language for traceability information in conjunction with metrics. The language contains a meta-model and a model library. TraceML allows to define customized traceability models in an efficient way. The integration pipeline is realized using TraceML as lingua franca in all processing steps: From the extraction of traceability information to its transformation and integrated representation. We used the Eclipse Modeling Framework to define the TraceML models on each meta-model level. Furthermore, we used the Modeling Workflow Engine for model transformations and Eclipse CDO as our model repository. A set of wide-spread tools for software engineering are connected to the integration pipeline including Subversion, Eclipse, Jira, Enterprise Architect and Maven.
The main contribution of our group to this project is the analysis core, i.e., the design and implementation of a domain-specific language for graph-based traceability analysis. Our Traceability Query Language (TracQL) significantly reduces the effort that is necessary to implement traceability analyses. This is crucial for both industry and the research community as lack of expressiveness and inefficient runtimes of other known approaches used to hinder the implementation of traceability analysis. TracQL eases not only the extraction, but also the analysis of traceability data using graph traversals that are denoted in a concise functional programming style. The language itself is built on top of Scala, a multi-paradigm programming language, and was successfully applied to several real-world industrial projects.
In 2015, we evaluated and documented our approach in order to emphasize its core attributes and to show its effectiveness. The three core attributes are:

  • Representation independence: TracQL is adaptable to various data sources at which their data types are available statically typed.
  • Modularity: the approach is both modifiable and extendable in terms of structure and operations.
  • Applicability: the language has a better expressiveness and performance than other approaches.
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