Automatic frequency assignment for cellular telephones using constraint satisfaction techniques

We study the problem of automatic frequency assignment for cellular telephone systems. The frequency assignment problem is viewed as the problem to minimize the unsatisfied soft constraints in a constraint satisfaction problem (CSP) over a finite domain of frequencies involving co-channel, adjacent channel, and co-site constraints. The soft constraints are automatically derived from signal strength prediction data. The CSP is solved using a generalized graph coloring algorithm. Graph-theoretical results play a crucial role in making the problem tractable. Performance results from a real-world frequency assignment problem are presented. We develop the generalized graph coloring algorithm by stepwise refinement, starting from DSATUR and augmenting it with local propagation, constraint lifting, intelligent backtracking, redundancy avoidance, and iterative deepening

Thesis Proposal : Evaluation of Combination Strategies for Practical Testing

A number of combination strategies have been proposed during the last fifteen years. Combination strategies are test case selection methods where test cases are identified by combining interesting values of the test object's input parameters. Although some results, achieved from small isolated experiments and investigations, point in the direction that these methods are useful in practical testing. Few attempts have been made to investigate these methods under realistic testing conditions. We outline a thesis proposal that is an attempt to determine if combination strategies are feasible alternatives to the currently used test case selection methods in practical testing. For combination strategies to be feasible alternatives to use in practical testing we require two things. Firstly, the combination strategies need to be effective in finding faults, at least as effective as currently used methods. Secondly, the cost per fault found when using combination strategies should not exceed the corresponding cost for the currently used methods. To investigate the effectiveness and efficiency of combination strategies we need to establish a benchmark from practical testing and then compare that with how combination strategies perform in the same or similar situations. Further, we need a testing process targeted for the use of combination strategies to be able to assess the complete cost of using combination strategies. Thus, an important part of this research project is to develop a combination strategies testing process. In particular, the activities of selecting combination strategies to use and transforming the requirements on the test object into a format suitable for combination strategies are focused on. These activities are specific to combination strategies and not very well understood. The methods used for achieving our research goal include literature surveys, investigation of the state-of-practice, with respect to used test case selection methods and cost of testing, experiments, tool implementations, and proof-of-concept, in the form of a case study. In addition to the direct results of our investigations we expect this research to result in detailed information about how to use the suggested test process. This information will include work instructions covering the manual parts. The process information will also include functional descriptions of the tools as well as interface descriptions of the input and output formats of each tool. These tool descriptions will make the test process generic in the sense that alternative tool implementations can be evaluated keeping everything else constant

Handling combinatorial explosion in software testing

In this thesis, the overall conclusion is that combination strategies, (i.e., test case selection methods that manage the combinatorial explosion of possible things to test), can improve the software testing in most organizations. The research underlying this thesis emphasizes relevance by working in close relationship with industry. Input parameter models of test objects play a crucial role for combination strategies. These models consist of parameters with corresponding parameter values and represent the input space and possibly other properties, such as state, of the test object. Test case selection is then defined as the selection of combinations of parameter values from these models. This research describes a complete test process, adapted to combination strategies. Guidelines and step-by-step descriptions of the activities in process are included in the presentation. In particular, selection of suitable combination strategies, input parameter modeling and handling of conflicts in the input parameter models are addressed. It is also shown that several of the steps in the test process can be automated. The test process is validated through a set of experiments and case studies involving industrial testers as well as actual test problems as they occur in industry. In conjunction with the validation of the test process, aspects of applicability of the combination strategy test process (e.g., usability, scalability and performance) are studied. Identification and discussion of barriers for the introduction of the combination strategy test process in industrial projects are also included. This research also presents a comprehensive survey of existing combination strategies, complete with classifications and descriptions of their different properties. Further, this thesis contains a survey of the testing maturity of twelve software-producing organizations. The data indicate low test maturity in most of the investigated organizations. Test managers are often aware of this but have trouble improving. Combination strategies are suitable improvement enablers, due to their low introduction costs

Handling Constraints in the Input Space when Using Combination Strategies for Software Testing

This study compares seven different methods for handling constraints in input parameter models when using combination strategies to select test cases. Combination strategies are used to select test cases based on input parameter models. An input parameter model is a representation of the input space of the system under test via a set of parameters and values for these parameters. A test case is one specific combination of values for all the parameters. Sometimes the input parameter model may contain parameters that are not independent. Some sub-combinations of values of the dependent parameters may not be valid, i.e., these sub-combinations do not make sense. Combination strategies, in their basic forms, do not take into account any semantic information. Thus, invalid sub-combinations may be included in test cases in the test suite. This paper proposes four new constraint handling methods and compares these with three existing methods in an experiment in which the seven constraint handling methods are used to handle a number of different constraints in different sized input parameter models under three different coverage criteria. All in all, 2568 test suites with a total of 634,263 test cases have been generated within the scope of this experiment

Combination testing strategies: A survey

Combination strategies are test case selection methods that identify test cases by combining values of the different test object input parameters based on some combinatorial strategy. This survey presents 16 different combination strategies, covering more than 40 papers that focus on one or several combination strategies. This collection represents most of the existing work performed on combination strategies. This survey describes the basic algorithms used by the combination strategies. Some properties of combination strategies, including coverage criteria and theoretical bounds on the size of test suites, are also included in this description. This survey paper also includes a subsumption hierarchy that attempts to relate the various coverage criteria associated with the identified combination strategies

An Evaluation of Combination Strategies for Test Case Selection

This paper presents results from a comparative evaluation of five combination strategies. Combination strategies are test case selection methods that combine “interesting ” values of the input parameters of a test subject to form test cases. This research comparatively evaluated five combination strategies; the All Combination strategy (AC), the Each Choice strategy (EC), the Base Choice strategy (BC), Orthogonal Arrays (OA) and the algorithm from the Automatic Efficient Test Generator (AETG). AC satisfies n-wise coverage, EC and BC satisfy 1-wise coverage, and OA and AETG satisfy pair-wise coverage. The All Combinations strategy was used as a “gold standard ” strategy; it subsumes the others but is usually too expensive for practical use. The others were used in an experiment that used five programs seeded with 128 faults. The combination strategies were evaluated with respect to the number of test cases, the number of faults found, failure size, and number of decisions covered. The strategy that requires the least number of tests, Each Choice, found the smallest number of faults. Although the Base Choice strategy requires fewer test cases than Orthogonal Arrays and AETG, it found as many faults. Analysis also shows some properties of the combination strategies that appear significant. The two most important results are that the Each Choice strategy is unpredictable in terms of which faults will be revealed, possibly indicating that faults are found by chance, and that the Base Choice and the pair-wise combination strategies to some extent target different types of faults

An Evaluation of Combination Strategies for Test Case Selection

In this report we present the results from a comparative evaluation of five combination strategies. Combination strategies are test case selection methods that combine interesting values of the input parameters of a test object to form test cases. One of the investigated combination strategies, namely the Each Choice strategy, satisfies 1-wise coverage, i.e., each interesting value of each parameter is represented at least once in the test suite. Two of the strategies, the Orthogonal Arrays and Heuristic Pair-Wise strategies both satisfy pair-wise coverage, i.e., every possible pair of interesting values of any two parameters are included in the test suite. The fourth combination strategy, the All Values strategy, generates all possible combinations of the interesting values of the input parameters. The fifth and last combination strategy, the Base Choice combination strategy, satisfies 1-wise coverage but in addition makes use of some semantic information to construct the test cases. Except for the All Values strategy, which is only used as a reference point with respect to the number of test cases, the combination strategies are evaluated and compared with respect to number of test cases, number of faults found, test suite failure density, and achieved decision coverage in an experiment comprising five programs, similar to Unix commands, seeded with 131 faults. As expected, the Each Choice strategy finds the smallest number of faults among the evaluated combination strategies. Surprisingly, the Base Choice strategy performs as well, in terms of detecting faults, as the pair-wise combination strategies, despite fewer test cases. Since the programs and faults in our experiment may not be representative of actual testing problems in an industrial setting, we cannot draw any general conclusions regarding the number of faults detected by the evaluated combination strategies. However, our analysis shows some properties of the combination strategies that appear significant in spite of the programs and faults not being representative. The two most important results are that the Each Choice strategy is unpredictable in terms of which faults will be detected, i.e., most faults found are found by chance, and that the Base Choice and the pair-wise combination strategies to some extent target different types of faults