Cosine Similarity Based Clustering For Software Testing Using ...

Comparison of Clustering Algorithms in Text Clustering Tasks

The Cosine Similarity was used as the similarity measure between the texts. The clustering tasks were realized over the PAN dataset and three different ...

The proposed technique employs different similarity metrics, including Cosine Similarity, Euclidean Distance, Jaccard Similarity, Manhattan ...

Dynamic Document Clustering Using k mean with Cosine Similarity ...

In software engineering, component based software development or component based software engineering is a more effective and reuse based approach. This ...

A Reuse-oriented Clustering Method for Test Cases - DSA 2022

Vector based privacy-preserving document similarity with LSA[C]//2017 IEEE 9th International Conference on. Communication Software and Networks (ICCSN). IEEE, ...

For better faced clustering, a cosine similarity measure is estimated for all the nodes on the network. Based on the similarity measure among the nodes, the ...

Dynamic document clustering using k mean with cosine similarity for ...

Request PDF | Dynamic document clustering using k mean with cosine similarity for effective software test cases reuse | Text retrieval is a popular ...

Clustering Techniques and the Similarity Measures used in Clustering

clustering approach in contrast to Cosine, Jaccard distance ... To work with pattern based similarity clustering using an efficient data association method called.

Index Terms—Software testing, Test case similarity, Clustering. ♢. 1 ... cosine metrics) to measure the similarity of test cases based.

2.3. Clustering — scikit-learn 1.5.2 documentation

First the Voronoi diagram of the points is calculated using the current centroids. Each segment in the Voronoi diagram becomes a separate cluster. Secondly, the ...

The system can use clustering based on the feature vectors to find test cases which are similar enough to be considered redundant. These requirements are the ...

Abstract—With the continuous evolution of software systems, test suites ... [7] combined clustering with expert knowledge to achieve scalable prioritization.

Index Terms—Software testing, Test case similarity, Clustering. ♢. 1 ... cosine metrics) to measure the similarity of test cases based.

clustering: are Euclidean distance, cosine similarity ... Dynamic random testing with test case clustering and distance-based parameter adjustment ...

A set theory based similarity measure for text clustering and ...

Similarly, Cosine similarity finds similarity between each document pair using the angle between their vectors [10]. The triangle distance is ...

Dynamic Requirement Clustering of Requirement with Usable Test ...

Clustering with Cosine similarity c. Ranking the clusters d. Sentence ... "How to effectively use topic models for software engineering tasks? an approach based.

From word embeddings to text similarities for improved semantic ...

The optimal number of clusters is manually selected by software engineers based on the ... Dutoit, “ Object-Oriented Software Engineering Using. UML, Patterns, ...

Evaluation of the correlation between test cases dependency and ...

The result of the similarity calculation is then clustered using the HDBSCAN clustering algorithm. The clusters would represent test cases' ...

Effectiveness of Different Similarity Measures for Text Classification ...

tried to compare the cosine and fuzzy similarity measure using the k-means algorithm. Muhammad Rafiet. al. proposed the topic map based similarity measure, ...

Empirical Study of the Effects of Different Similarity Measures on ...

More recently, similarity measures are also applied to software testing. Similarity-based test case prioritization techniques depend on ...

How to use cosine similarity in clustering - Quora

Using cosine similarity rather than Euclidean distance is referred to as spherical k-means. You have to modify two parts of the algorithm.