Regression testing is a maintenance level activity performed on a modified program to instill confidence in the software's reliability. Prioritization of test case arranges the regression test suite to detect the ...
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Regression testing is a maintenance level activity performed on a modified program to instill confidence in the software's reliability. Prioritization of test case arranges the regression test suite to detect the faults earlier in the testing process. The test cases necessary for validating the recent changes and finding the maximum faults in minimum time are selected. In this manuscript, an optimization algorithm (bee Algorithm) based on the intelligent foraging behavior of honey bee swarm has been proposed that can enhance the rate of fault detection in test case prioritization. The bee algorithm, along with the fuzzy rule base, reduces the test cases' volume by selecting the test cases from the pre-existing test suite. The proposed algorithm developed for enhancing the fault detection rate in minimum time is inspired by the behavior of two types of worker bees, namely scout bees and forager bees. These worker bees are responsible for the maintenance, progress, and growth of the colony. The proposed approach is implemented on two projects. The prioritization result is quantified by using the average percentage of fault detection (APFD) metric. Compared with other existing prioritization techniques like no prioritization, reverse prioritization, random prioritization, and previous work, the proposed algorithm outperforms all in fault detection rate. The effectiveness of the proposed algorithm is represented by using the APFD graphs and charts.
From the very beginning the most promising AI methods are inspired by human environment and nature. Especially, collective intelligence of non-human societies can surprised researchers and developers of new solutions....
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ISBN:
(数字)9783030965921
ISBN:
(纸本)9783030965921;9783030965914
From the very beginning the most promising AI methods are inspired by human environment and nature. Especially, collective intelligence of non-human societies can surprised researchers and developers of new solutions. It is matter of specific abilities of particular species oriented on cooperation but moreover awareness of precisely defined goals and resources used in very optimal ways. For example we may admire methods of building cells by honey bee society and organizing of their works;therefore for example problems of tasks planning or optimization of collection of pollen by bees are being solved through certain sort of common intuition and collective intelligence. Proposed bees algorithm (as an example of swarm algorithms) has been applied in continuous domains (optimization of neural networks) or combinatorial ones (scheduling jobs for a machine). The goal of this paper is presentation of collective intelligence useful in relatively new directions: energy acquisition and selected processes assuring sustainable development. Both directions seem to be very innovative and promising-especially in the ecosystems context.
In this paper we present a modified version of an existing honey bee optimization algorithm: the modified fast marriage in honey bee optimization (MFMBO). Then we compare performances of this new algorithm and three e...
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ISBN:
(纸本)9781424481835
In this paper we present a modified version of an existing honey bee optimization algorithm: the modified fast marriage in honey bee optimization (MFMBO). Then we compare performances of this new algorithm and three existing bee algorithms, i.e. the artificial bee colony (ABC), the queen bee (QB), and the fast marriage in honey bee optimization (FMBO) on four benchmark functions for various numbers of variables up to 100. The obtained results show that the modified algorithm is faster than the others in most cases. Especially for Griewank and Schwefel functions by increasing precision of answer and number of variables, the difference between the speeds of the FMBO and MFMBO becomes prominent. In general the MFMBO algorithm is quite competitive with the other mentioned algorithms.
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