Prioritization of test scenarios using hybrid genetic algorithm based on UML activity diagram

2015 ◽  
Author(s):  
Xinying Wang ◽  
Xiajun Jiang ◽  
Huibin Shi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Activity Diagram ◽  
Uml Activity Diagram ◽  
Test Scenarios

Test Path Prioritization from UML Activity Diagram Using a Hybridized Approach

2018 ◽  
Vol 8 (1) ◽  
pp. 83-96 ◽  
Author(s):  
Gargi Bhattacharjee ◽  
Sudipta Dash
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Critical Path ◽  
Optimization Technique ◽  
The Other ◽  
Test Cases ◽  
Activity Diagram ◽  
Apply Genetic ◽  
Uml Activity Diagram ◽  
Uml Models

Software testing is regarded as a pivotal approach to realize a high reliable product. To check for the correctness of results, we require appropriate test cases. UML models are largely used to depict the specifications for software development. Test cases are created independently and based on the sequence of occurrence in the diagrams; they lead to corresponding test paths in the program. In this paper, we have analyzed an activity diagram, consisting of concurrent activities, for generating test paths. The obtained test paths are therefore required to be ranked. We have demonstrated that it is conceivable to apply Genetic Algorithm procedures alongside Ant Colony Optimization technique for not only finding the most critical path but also prioritizing the other paths too for enhancing the effectiveness of software testing.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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