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Abstract. Evolutionary Testing (ET) has been shown to be very successful for testing real world applications [10]. The original ET approach focuses on searching for a high coverage of the test object by generating separate inputs for single function calls. We have identified a large set of real world application for which this approach does not perform well because only sequential calls of the tested function can reach a high structural coverage (white box test) or can check functional behavior (black box tests). Especially, control software which is responsible for controlling and constraining a system cannot be tested successfully with ET. Such software is characterized by storing internal data during a sequence of calls. In this paper we present the Evolutionary Sequence Testing approach for white box and black box tests. For automatic sequence testing, a fitness function for the application of ET will be introduced, which allows the optimization of input sequences that reach a high coverage of the software under test. The authors also present a new compact description for the generation of real-world input sequences for functional testing. A set of objective functions to evaluate the test output of systems under test have been developed. These approaches are currently used for the structural and safety testing of car control systems.

The use of evolutionary algorithms for calculation of the optimal control of the states of a greenhouse system will be presented. The integrated model employed (greenhouse climate, crop growth, outside weather conditions and control equipment) predicts temperature, air humidity and CO 2 concentration in a time interval of 15-60 minutes (short time-scale model). The paper presents the optimization of the control of the greenhouse climate to maximize the profit under certain constraints (for instance, prevention of stress for the crops) using evolutionary algorithms. By incorporation of problem specific knowledge into the evolutionary algorithm better results were produced in a shorter time. The results of optimization for optimal control using real world weather data are shown.

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Whereas the verification of non-safety-related, embedded software typically focuses on demonstrating that the implementation fulfills its functional requirements, this is not sufficient for safety-relevant systems. In this case, the control software must also meet application-specific safety requirements.Safety requirements typically arise from the application of hazard and/or safety analysis techniques, e.g. FMEA, FTA or SHARD. During the downstream development process it must be shown that these requirements cannot be violated. This can be achieved utilizing different techniques. One way of providing evidence that violations of the safety properties identified cannot occur is to thoroughly test each of the safety requirements. This paper introduces Evolutionary Safety Testing (EST), a fully automated procedure for the safety testing of embedded control software. EST employs extended evolutionary algorithms in an optimization process which aggressively tries to find test data sequences that cause the test object to violate a given safety requirement.A compact description formalism for input sequences for safety testing is presented, which is compatible with description techniques used during other test process stages. This compact description allows 1) an efficient application of evolutionary algorithms (and other optimization techniques) and 2) the description of long test sequences necessary for the adequate stimulation of real-world systems. The objective function is designed in such a way that optimal values represent test data sequences which violate a given safety requirement. By means of repeated input sequence generation, software execution and the subsequent evaluation of the objective function each safety requirement is extensively tested.The use of EST for the safety testing of automotive control software is demonstrated using safety requirements of an adaptive cruise control (ACC) system.The EST approach can easily be integrated into an overall software test strategy which combines different test design techniques with specific test objectives.