Detlef Streitferdt

Details of formalized relations in feature models using OCL

System families are a form of high level reuse of development assets in a specific problem domain, by making use of commonalities and variabilities. To represent assets belonging to the core of the family and assets belonging to variable parts, feature modeling is a widely used concept. Consistency checking in feature models is not yet addressed appropriately by current methods. The paper gives a brief overview of feature modeling and elaborates the problems of current approaches. Based on the applications of these approaches within an ongoing research project, the paper proposes a formalized definition for feature modeling using the Object Constraint Language (OCL) and a set of associations and constraints to be used in the feature model. The relations between features in the feature model and features to external assets are examined and a way to formally handle these relations is presented as a result of a research project.

An approach for reverse engineering of design patterns

For the maintenance of software systems, developers have to completely understand the existing system. The usage of design patterns leads to benefits for new and young developers by enabling them to reuse the knowledge of their experienced colleagues. Design patterns can support a faster and better understanding of software systems. There are different approaches for supporting pattern recognition in existing systems by tools. They are evaluated by the Information Retrieval criteria precision and recall. An automated search based on structures has a highly positive influence on the manual validation of the results by developers. This validation of graphical structures is the most intuitive technique. In this paper a new approach for automated pattern search based on minimal key structures is presented. It is able to detect all patterns described by the GOF [15]. This approach is based on positive and negative search criteria for structures and is prototypically implemented using Rational Rose and Together.

Modeling Variability for Object-Oriented Product Lines

The concept of a software product line is a promising approach for increasing planned reusability in industry. For planning future requirements, the integration of domain analysis activities with software development for reusability turned out to be necessary, both from a process and from an economic point of view. In this context, variability of requirements in a domain is expressed by feature models. Feature models enable planning and strategic decisions both for architectural and for component development. By expressing feature dependencies, feature models are used to partition the architecture and the implementation. For industrial use, appropriate methods for modeling variability in requirements, design and implementation as well as tools for supporting feature models and for integrating them with other models are needed. The ECOOP workshop explored the possibilities and limitations of feature models and supporting methods. Its fully reviewed contributions aim at improving the feature model usage as well as the integration into the software development process. Improving industrial applicability of feature modeling and methods is an important goal. This paper provides a summary of the discussion and presents the major results as well as important questions and issues identified for future research.

Model Driven Development Challenges in the Automation Domain

Model driven development has evolved to a mature methodology and technology usable for some industrial settings. Within the automation domain it is an upcoming approach. This paper addresses challenges present in the automation domain when it comes to the usage of model driven development. Quality, life cycle, legacy systems, mental approach and safety challenges are briefly discussed.

Searching design patterns in source code

Maintenance is a time consuming activity within software development and it requires a good understanding of the system in question. It is hard or even impossible to understand poorly documented legacy systems. Nevertheless, developers try to understand unknown object oriented systems by analysing the source code to recover the architecture of the system, which is a hard task since the dependencies between the classes cannot be recovered good enough. Here, the knowledge about design patterns can help developers to understand the underlying architecture faster. We analysed existing pattern search approaches and compared them by their recall and precision values, metrics out of the information retrieval domain. As a result we developed own pattern search algorithms for the 23 design pattern described by Gamma et al. (1995). This fast abstract briefly explains the basics of our pattern search and describes first results of the search algorithms developed as a Java plug-in for the Together IDE. This work was funded by the BMBF (2004) and is part of the InPULSE (2004) project.

Traceability for system families

System families are an idea of software reuse in a specific problem domain. Existing methods have little requirements engineering support for system family development. This paper proposes a requirements meta-model for system family development. Traceability throughout the model elements is a necessary pre-condition for preserving the consistency of the complete family model during development and is a main issue in this paper as well as for software development in general. Family development based on the meta-model guarantees traceability by the inclusion of all development artifacts in a single and consistent model.

On the development of intelligent optical inspections

The automated optical inspection (AOI) has replaced the manual optical inspection of printed circuit boards (PCBs) in nearly all manufacturing lines. Programming the inspection machines is time consuming and requires a good understanding of the optical inspection domain and printed circuit boards. The process of creating such inspection programs needs to be simplified to enable the user to get to inspection results easier and faster. Over the last few years better and affordable computational hardware helped developing the field of artificial neural networks, called deep learning, to an extent, that it surpassed other state of the art approaches and is used in many optical classification tasks since then. With the ongoing trend of Industry 4.0 AOI systems started not only producing but also storing vast amounts of data. This has the potential to aid the improvement of the inspection machines. This paper will discuss key aspects of AOI as well as basics of deep learning and proposes a new way to integrate these techniques for the manufacturers and users of AOI machines. Additionally, the process of programming AOI inspections will be faster.

Testing with Large Parameter Sets for the Development of Embedded Systems in the Automation Domain

Testing of current devices in the automation domain cannot be done mathematically exhaustive due to the huge number of possible test cases and, even more important, the tremendous amount of time for the execution of all test cases.Thus, a dramatic reduction is needed in the testing domain.This paper discusses two possibilities for the reduction of test case execution time by reducing the number of ”parameter set” permutations, recursive backtracking and pairwise testing.Testing of devices in the automation domain is based on their behavior (black box testing) and the parameters of the devices,which influence the behavior.The combination of a parameter model including constraints between parameters, recursive backtracking and pairwise testing can result in the reduction of the parameter permutation space by more than 99%. This paper presents the current status of this reduction and proposes a future direction towards a structured reduction of test cases based on the reduction of the ”parameter set” permutations.

Test Case Mutation in Hybrid State Space for Reduction of No-Fault-Found Test Results in the Industrial Automation Domain

During development of a device a large set of testcases is executed to ensure the qualitative requirements. Nevertheless because of timing issues it is not possible to perform all possible test cases and therefore it is not possible to guarantee a product that works always as expected by the end user.Finding the root cause of failures in returned devices is still largely manual work by an expert because the exact system and environment state is not known.In this paper we present an approach which allows automatic mutation of test cases for hybrid systems to reproduce failures based on vague user descriptions.

A Reasoning Based Knowledge Model for Business Process Analysis

The article presents the ontology based knowledge model iKnow that can automatically draw conclusions and integrate aspects of machine learning. Due to the knowledge-intensive nature of the consulting industry, the abstract reasoning based knowledge model can be used specifically for knowledge processing and decision support within a consulting project. There is a multitude of potential applications for iKnow in the realm of consulting. Business process analysis was chosen as a pilot application, since many consulting projects in the problem analysis and problem solving phase, require a comprehensive knowledge of business processes. In this paper it is outlined how iKnow can be used for an automated analysis of business process models. We describe the basic structure of the knowledge model as a business process analyzing tool and present a suitable demonstration. It is worth mentioning that iKnow does not necessarily rely on log-files or other data input from process-supporting IT-systems. In this way, and through the generality of its ontology based structure and reasoning capabilities, it is far more broadly applicable than current process mining solutions.