Analysis and Testing of Ajax-based Single-page Web Applications

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3.3.5 Target UI Model Transformation For each target system, a meta-model has to be created and the corresponding transformation between the single-page meta-model language and the platform-specific language defined. The advantage of having an abstract user interface model is that we can transform it to different Ajax settings. We have explored a number of Ajax frameworks such as Backbase, Echo2, and GWT, and have started conducting research to adopt a model-driven approach to Ajax (Gharavi et al., 2008). 3.4 Navigational Path Extraction Our ajaxification approach starts by reconstructing the paths that users can follow when navigating between web pages. This requires that we group pages that are sufficiently similar and directly reachable from a given page. For example, a web page A could contain 7 links, 3 of which are similar. We cluster those 3 pages, and look if the links contained in those 3 pages, together, could be clustered, and so on. This way we build clusters along with the navigational paths. In this section, we discuss our specific notion of web page similarity, and the steps that we follow to compute the clusters. 3.4.1 Page Classification Web pages can be classified in many different ways depending on the model needed for the target view. Draheim et al. (Draheim et al., 2005) list some of possible classification notions. In this chapter, our target view focuses on structural classification. Tonella and Ricca (Ricca and Tonella, 2001; Tonella and Ricca, 2004) present three relevant notions of classification: • Textual Identity considers two pages the same if they have exactly the same HTML code, • Syntactical Identity groups pages with exactly same structure, ignoring the text between tags, according to a comparison of the syntax trees, • Syntactical Similarity classifies pages with similar structure, according to a similarity metric, computed on the syntax trees of the pages. Textual and Syntactical Identity classification notions have limited capabilities in finding pages that belong to a certain category as they look for exact matches. Syntactical Similarity is the notion that can help us cluster pages into useful groups by defining a similarity threshold under which two pages are considered clones. We propose a new approach based on schema-based similarity. 60 3.4. Navigational Path Extraction
Classic Web application Retrieve pages HTML pages For each page Page Page Links Search Nav Links Extract Schema Page Schema Detect Schema Clones Pair Page Clones Cluster Clones Clusters Figure 3.3 Schema-based clustering process. 3.4.2 Schema-based Similarity Many web clustering approaches (Di Lucca et al., 2002b; De Lucia et al., 2004a; Ricca and Tonella, 2003) base their similarity degree on the computation of the edit distance between the syntax trees of web pages. This approach, although useful, has a limited capability to group HTML pages that have similar presentational structure. For instance, consider two pages, the first page having two table rows and the second seven rows with the same structure and number of cells. On the screen, we instantly classify these two under one category, but the edit distance of these two pages could be quite high and as thus the classification metric would not classify them in one cluster. Increasing the metric threshold is not an option because that results in an increase in the number of incorrectly combined pages. To overcome this issue, our approach relies on a comparison of the explicit schemas of pages. This means, instead of comparing the syntax trees of pages, we first reverse engineer the schemas of the pages and then compute the edit distance of the corresponding schemas. Two pages are considered clones if their schemas are similar. Going back to our example, the two pages can now be clustered correctly as a table with two row elements has the same schema as a table with seven row elements. Chapter 3. Migrating Multi-page Web Applications to Ajax Interfaces 61
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Analysis and Testing of Ajax-based
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Contents Preface List of Acronyms i
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3.3.4 Single-page UI Model Definiti
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6.3.2 Trigger . . . . . . . . . . .
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Preface our years have passed since
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List of Acronyms ASP Active Server
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Introduction A ccording to recent s
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Figure 1.2 Screen shot of the Mosai
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Figure 1.4 Screen shot of OpenLaszl
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DOM APIs, a technique that was call
Page 21 and 22: Figure 1.6 Screen shot of Google Su
Page 23 and 24: Figure 1.7 Screen shot of Google Do
Page 25 and 26: Reengineering Analysis & Testing Ar
Page 27 and 28: client/server and network-based env
Page 29 and 30: To the best of our knowledge, at th
Page 31 and 32: Table 1.1 ❳❳ ❳ ❳ ❳❳ Cha
Page 33 and 34: equally and we chose to use an alph
Page 35 and 36: A Component- and Push-based Archite
Page 37 and 38: Page Internet Application aRchitect
Page 39 and 40: XML message containing directives t
Page 41 and 42: server sends the data to the client
Page 43 and 44: • Code reuse: shared implementati
Page 45 and 46: Rest provides Ajax demands Large-gr
Page 47 and 48: work well, will lose customers (Off
Page 49 and 50: communication between client and se
Page 51 and 52: GWT uses an RPC style of calling se
Page 53 and 54: DOM Client Browser event update Aja
Page 55 and 56: Client Server Legend Interaction po
Page 57 and 58: User Interactivity User-perceived L
Page 59 and 60: User Interactivity User-perceived L
Page 61 and 62: and does not comply with the Resour
Page 63 and 64: 2.8.9 Design Models Figure 2.8 show
Page 65 and 66: of user tasks as first class entiti
Page 67 and 68: Migrating Multi-page Web Applicatio
Page 69 and 70: Web App 1 Page deltaChange viewChan
Page 71: gational and structural model of th
Page 75 and 76: Algorithm 1 1: procedure start (Pag
Page 77 and 78: 3.5.2 Identifying Elements The list
Page 79 and 80: Classification # of pages Home (Ind
Page 81 and 82: Figure 3.7 A candidate UI component
Page 83 and 84: target system. Even though the tech
Page 85 and 86: Third, we proposed a schema-based c
Page 87 and 88: Performance Testing of Data Deliver
Page 89 and 90: Figure 4.1 A screenshot taken from
Page 91 and 92: the user. Ideally, the pulling inte
Page 93 and 94: Streaming Figure 4.3 shows how the
Page 95 and 96: 4.4 Experimental Design In this sec
Page 97 and 98: Mean Publish Trip-time (MPT) We def
Page 99 and 100: tributes to the complexity of contr
Page 101 and 102: 8. Start the publisher and begin pu
Page 103 and 104: Figure 4.8 Sample Stock Ticker Appl
Page 105 and 106: Figure 4.9 Mean Publish Trip-time C
Page 107 and 108: 4.6.3 Received Publish Messages Fig
Page 109 and 110: Figure 4.12 Mean Number of Received
Page 111 and 112: Figure 4.13 Percentage of data item
Page 113 and 114: with pull is higher than push. This
Page 115 and 116: this scenario, a data item will onl
Page 117 and 118: With the pull approach, achieving t
Page 119 and 120: Crawling Ajax by Inferring User Int
Page 121 and 122: discusses the findings and open iss
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5.3 A Method for Crawling Ajax The
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Robot click UI Browser generate cli
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5.3.5 Creating States After firing
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1 crawl MyAjaxSite { 2 url: http://
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The generator uses JTidy 10 to pret
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G1. For the experiment we have manu
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19247 examined candidate elements,
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ich interactivity and responsivenes
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5.8 Related Work The concept behind
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Invariant-Based Automatic Testing o
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has some support for client-side sc
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6.3.2 Trigger Once we are able to d
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a new edge is created on the graph
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Other Invariants. In line with the
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6.7.1 Oracle Comparators After each
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Algorithm 4 Pre/postCrawling hooks
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LOC Server-side LOC Client-side DOM
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todo items, removing all items inst
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Failure Cause Violated Invariant In
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plications) is that it is very hard
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1. A series of fault models that ca
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Conclusion ith the advent of Ajax t
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out to be an appropriate framework
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plays a more prominent role in the
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correct behavior. Violations in the
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A great deal of our work has focuse
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A Single-page Ajax Example Applicat
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1 if(navigator.appName == "Microsof
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Figure A.5 The run-time DOM instanc
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Figure A.7 The request/response tra
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Samenvatting ⋆ Analyse en Testing
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een dergelijk model te creëren, do
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Bibliography Abrams, M., Phanouriou
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Bouras, C. and Konidaris, A. (2005)
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De Lucia, A., Scanniello, G., and T
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Garofalakis, M., Gionis, A., Rastog
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Levenshtein, V. L. (1996). Binary c
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Netscape (1995). An exploration of
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Sprenkle, S., Pollock, L., Esquivel
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Wohlin, C., Runeson, P., Höst, M.,
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Curriculum Vitae Personal Data Full
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A. Dijkstra. Stepping through Haske
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M. A. Abam. New Data Structures and
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Analysis and Testing of Ajax-based Single-page Web Applications

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