[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160261-en":3,"doc-seo-160261-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},160261,549768702563,"Fahsai","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Does Principal Component Analysis Improve Cluster-Based Analysis?","Researchers in dynamic program analysis commonly use cluster analysis on execution profiles with extremely high dimensionality, sometimes involving thousands to hundreds of thousands of profiling elements. This work examines whether reducing dimensionality via principal component analysis (PCA) can improve clustering effectiveness as a preprocessing step. PCA is evaluated on two cluster-based techniques: identifying coincidentally correct tests and minimizing test suites. Results show a positive impact for the first technique, while the effect on the second remains inconclusive, requiring further study.","2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops  \nDoes Principal Component Analysis Improve Cluster-Based Analysis?  \nJoan Farjo, Rawad Abou Assi, Wes Masri, and Fadi Zaraket Department of Electrical and Computer Engineering  \nAmerican University of Beirut  \nBeirut, Lebanon  \n{jmf09, ria21, wm13, [fz11}@aub.edu.lb](fz11}@aub.edu.lb)  \nAbstract- Researchers in the dynamic program analysis field have extensively used cluster analysis to address various problems. Typically, the clustering techniques are applied onto execution profiles having high dimensionality (i.e., involving a large number of profiling elements), sometimes in the order of thousands or even hundreds of thousands. Our concern is that the high number of profiling elements might diminish the effectiveness of the clustering process, which led us to explore the use of dimensionality reduction techniques as apreprocessing step to clustering.  \nSpecifically, in this work, we used PCA (Principal Component Analysis) as a dimensionality reduction technique and investigated its impact on two cluster-based analysis techniques, one aiming at identifying coincidentally correct tests, and the other at test suite minimization. In other words, we tried to assess whether PCA improves cluster-based analysis. Our experimental results showed that the impact was positive on the first technique, but inconclusive on the second, which calls for further investigation in the future.  \nKeywords- PCA (Principal Component Analysis), cluster analysis, dimensionality reduction, test suite minimization, coincidental correctness.  \nI. INTRODUCTION  \nCluster analysis has been used in several areas of dynamic software analysis, such as test suite minimization, fault localization [8][10], and application-based intrusion detection [18] . The clustering techniques are applied onto execution profiles comprising profiling elements that varied in terms of complexity, e.g., statements, edges, def-uses, information flow pairs [17], slice pairs [11][19], and paths [21] . Also, typically these execution profiles exhibit high dimensionality, i.e., include thousands or even hundreds of thousands of profiling elements. Our concern is that the high number of profiling elements might diminish the effectiveness of the clustering process, which led us to explore the use of dimensionality reduction techniques as apreprocessing step to clustering.  \nThe goal of this work is to investigate the impact of dimensionality reduction on cluster-based dynamic program analyses, and specifically, the impact of PCA (Principal Component Analysis) [23] on two cluster-based analysis techniques, one aiming at identifying coincidentally correct tests, and the other at test suite minimization. In other words, we tried to answer the following question: “Does Principal Component Analysis Improve Cluster-Based Analysis?”.  \nWe first describe PCA and the basis behind it (Section II) . Then we describe our two experimental studies (Section  \nIII and IV) . Finally, Section V presents our conclusions and future work.  \nII. PRINCIPAL COMPONENT ANALYSIS  \nPrincipal Component Analysis (PCA) is an unsupervised and linear technique that reduces the dimensionality of a data set (possibly involving correlated variables) to a new set involving uncorrelated variables. The generated uncorrelated variables are called principal components (PCs) . The obtained set has the PCs ordered by the fraction of the total information/variation each retains. That is, the first PC captures as much of the variability present in the data set as possible, the second PC also captures as much of the variability but under the constraint of being uncorrelated with the previous (first) PC, and similarly for the subsequent PCs. PCA is typically used in situations where high dimensionality data needs to be reduced, therefore, after applying it, only specific PCs are considered and the remaining ones ignored according to t","cbCail8uQLzH4Q52","https://ap.wps.com/l/cbCail8uQLzH4Q52","pdf",152581,1,4,"English","en",105,"# Abstract\n# Introduction\n# Principal Component Analysis\n## Details\n## Experimental Studies\n# Conclusions and Future Work","[{\"question\":\"What problem does the paper address in cluster-based dynamic program analysis?\",\"answer\":\"It addresses concerns that very high dimensionality in execution profiles may weaken the effectiveness of clustering, motivating dimensionality reduction as preprocessing.\"},{\"question\":\"How does the paper use PCA in its experiments?\",\"answer\":\"It applies principal component analysis as a dimensionality reduction preprocessing step, then evaluates its impact on two cluster-based analysis techniques.\"},{\"question\":\"What were the experimental findings regarding the two techniques?\",\"answer\":\"The impact of PCA was positive for the technique focused on identifying coincidentally correct tests, but it was inconclusive for the technique focused on test suite minimization.\"}]","Does Principal Component Analysis Improve Cluster-Based Analysis? | PDF",1788052955,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"does-principal-component-analysis-improve-cluster-based-analysis","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/does-principal-component-analysis-improve-cluster-based-analysis/160261/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-30",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address in cluster-based dynamic program analysis?","Question",{"text":74,"@type":75},"It addresses concerns that very high dimensionality in execution profiles may weaken the effectiveness of clustering, motivating dimensionality reduction as preprocessing.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper use PCA in its experiments?",{"text":79,"@type":75},"It applies principal component analysis as a dimensionality reduction preprocessing step, then evaluates its impact on two cluster-based analysis techniques.",{"name":81,"@type":72,"acceptedAnswer":82},"What were the experimental findings regarding the two techniques?",{"text":83,"@type":75},"The impact of PCA was positive for the technique focused on identifying coincidentally correct tests, but it was inconclusive for the technique focused on test suite minimization.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]