176,312 research outputs found
Test Case Generation According to the Binary Search Strategy
One of the important tasks during software testing is the generation of test cases. Unfortunately, existing approaches to test case generation often have problems limiting their use. A problem of dynamic
test case generation approaches, for instance, is that a large number of iterations can be necessary to obtain test cases. This article introduces a formal framework for the application of the well-known search strategy of binary search in path-oriented test case generation and explains the binary search-based test case generation (BINTEST) algorithm
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
A quantum genetic algorithm with quantum crossover and mutation operations
In the context of evolutionary quantum computing in the literal meaning, a
quantum crossover operation has not been introduced so far. Here, we introduce
a novel quantum genetic algorithm which has a quantum crossover procedure
performing crossovers among all chromosomes in parallel for each generation. A
complexity analysis shows that a quadratic speedup is achieved over its
classical counterpart in the dominant factor of the run time to handle each
generation.Comment: 21 pages, 1 table, v2: typos corrected, minor modifications in
sections 3.5 and 4, v3: minor revision, title changed (original title:
Semiclassical genetic algorithm with quantum crossover and mutation
operations), v4: minor revision, v5: minor grammatical corrections, to appear
in QI
Tactical fixed job scheduling with spread-time constraints
We address the tactical fixed job scheduling problem with spread-time constraints.
In such a problem, there are a fixed number of classes of machines and a fixed number of groups of jobs. Jobs of the same group can only be processed by machines of a given set of classes. All jobs have their fixed
start and end times. Each machine is associated with a cost according to its machine class. Machines have spread-time constraints, with which each machine
is only available for L consecutive time units from the start time of the earliest job assigned to it. The objective is to minimize the total cost of the machines used to process all the jobs. For this strongly NP-hard problem, we develop a branch-and-price algorithm, which solves instances with up to 300 jobs, as compared with CPLEX, which cannot solve instances of 100 jobs.
We further investigate the influence of machine flexibility by computational experiments. Our results show that limited machine flexibility is sufficient in most situations
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