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

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index.html viewCategory.shtml@categoryId=DOGS.html viewCart.shtml.html signonForm.shtml.html help.html viewProduct.shtml@productId=K9-DL-01 newAccountForm.shtml.html viewItem.shtml@itemId=EST-18 addItemToCart.shtml@workingItemId=EST-10 removeItemFromCart.shtml@workingItemId=EST-20 checkout.shtml Figure 3.6 Reduced Clusters. In order to conduct a preliminary evaluation of the described reverse engineering process, we used two different methods, namely, our own schemabased similarity approach (MMS), and our own implementation of a syntax tree similarity (STS) approach as proposed by, e.g., (De Lucia et al., 2004b). We also used different thresholds to find out the best achievable results. In the first step of the reverse engineering process, pages were clustered along the navigational path (tree-based) and the navigational path was reduced by refining the clusters, as shown in Figure 3.5. Subsequently, as illustrated in Figure 3.6, in the second step, found pages in each cluster were reduced to a single node using the method described in 3.4.4. Afterwards, candidate UI components were calculated by applying the Differencing algorithm as described in Section 3.5. Figure 3.7 depicts viewing a candidate UI component (HTML code) in a browser, which is the result of going from the index page to the (dogs) category page. As a result, only that fraction of the category page that is unique with respect to the index page is reported. This way, we are able to visualize the delta changes (candidate single-page components) of the web interface by browsing the navigational path. The list of candidate components and the simplified navigational path help us model our target single-page interface in the Conallen UML extension (Conallen, 2003), which is shown in Figure 3.8. Our single-page (called Page), contains three UI components namely, Category, Cart, and SignOn. Navigation takes place by changing the view from one component to another. For instance, from the Category component we can change our view to go to the 68 3.7. Case Study
Figure 3.7 A candidate UI component (Product Category). Product component. This is a delta change, meaning only that part of the Page that contained the Category component will be updated to view the new Product component. 3.7.4 Evaluation Two well known metrics namely precision and recall were used to evaluate the results. Precision represents how accurately the clusters from the algorithm represent the reference classification. Recall measures how many pages in the reference classification are covered by clusters from the algorithm. We count only exact matches against the reference classification in the Relevant Clusters Retrieved (RCR) group. This means, if the algorithm finds a cluster which contains one or more extra (or one or more less) pages than the corresponding reference cluster, it is counted in the Irrelevant Clusters Retrieved (ICR). Other comparison techniques, such as the ones introduced by Koschke and Eisenbarth (Koschke and Eisenbarth, 2000) and Tzerpos and Holt (Tzerpos and Holt, 1999) could also have been chosen. However, we would expect similar results from these techniques as well. Table 3.2 shows the results. With the syntax tree similarity (STS) approach, the best recall value obtained was 82 % with a precision of 69 %, using a similarity threshold of 91 %. The meta-based similarity (MMS) approach, however, was able to find all 11 documented reference clusters with a recall and precision of 100 % using a similarity threshold of 98 %. Note that by increasing the threshold to 99 %, the precision and recall drop to 82 %. This behavior can be explained because the algorithm expects the schemas to be almost identical, and as a result very little difference in the corresponding pages is tolerated. This increases the number of false positives. Chapter 3. Migrating Multi-page Web Applications to Ajax Interfaces 69
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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
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Figure 1.6 Screen shot of Google Su
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Figure 1.7 Screen shot of Google Do
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Reengineering Analysis & Testing Ar
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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 and 72: gational and structural model of th
Page 73 and 74: Classic Web application Retrieve pa
Page 75 and 76: Algorithm 1 1: procedure start (Pag
Page 77 and 78: 3.5.2 Identifying Elements The list
Page 79: Classification # of pages Home (Ind
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
Page 123 and 124: 5.3 A Method for Crawling Ajax The
Page 125 and 126: Robot click UI Browser generate cli
Page 127 and 128: 5.3.5 Creating States After firing
Page 129 and 130: 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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