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

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The other four cases are all external Ajax sites and we have no access to their source-code. C4 is an Ajax site that can function as a tool for comparing the visual impression of different typefaces. C3 (online shop), C5 (sport center), and C6 (Gucci) are all single-page commercial sites with many clickables and states. 5.5.2 Experimental Design Our goals in conducting the experiment include: G1 Effectiveness: evaluating the effectiveness of obtaining high-quality results in retrieving relevant clickables including the ones dynamically injected into the DOM, G2 Correctness: assessing the quality and correctness of the states and static pages automatically generated, G3 Performance: analyzing the overall performance of our approach in terms of input size versus time, G4 Scalability: examining the capability of Crawljax on real sites used in practice and the scalability in crawling sites with thousands of dynamic states and clickables. Environment & Tool Configuration. We use a laptop with Intel Pentium M 765 processor 1.73GHz, with 1GB RAM and Windows XP to run Crawljax. Configuring Crawljax itself is done through a simple crawljax.properties file, which can be used to set the URL of the site to be analyzed, the tag elements Crawljax should look for, the depth level, and the similarity threshold. There are also a number of other configuration parameters that can be set, such as the directory in which the generated pages should be saved in. Output. We determine the average DOM string size, number of candidate elements, number of detected clickables, number of detected states, number of generated static pages, and performance measurements for crawling and generating pages separately for each experiment object. The actual generated linked static pages also form part of the output. Method of Evaluation. Since other comparable tools and methods are currently not available to conduct similar experiments as with Crawljax, it is difficult to define a baseline against which we can compare the results. Hence, we manually inspect the systems under examination and determine which expected behavior should form our reference baseline. 120 5.5. Case Studies
G1. For the experiment we have manually added extra clickables in different states of C1, especially in the delta updates, to explore whether clickables dynamically injected into the DOM can be found by Crawljax. A reference model was created manually by clicking through the different states in a browser. In total 16 clickables were noted of which 10 were on the top level, i.e., index state. To constrain the reference model for C2, we chose two product categories, namely CATS and DOGS, from the five available categories. We annotated 36 elements (product items) by modifying a JavaScript method which turns the items retrieved from the server into clickables on the interface. For the four external sites (C3–C6) which have many states, it is very difficult to manually inspect and determine, for instance, the number of expected clickables and states. Therefore, for each site, we randomly selected 10 clickables in advance by noting their tag name, attributes, and XPath expression. After each crawling process, we checked the presence of the 10 elements among the list of detected clickables. G2. After the generation process the generated HTML files and their content are manually examined to see whether the pages are the same as the corresponding DOM states in Ajax in terms of structure, style, and content. Also the internal linking of the static pages is manually checked. To test the clone detection ability we have intentionally introduced a clone state into C1. G3. We measure the time in milliseconds taken to crawl each site. We expect the crawling performance to be directly proportional to the input size which is comprised of the average DOM string size, number of candidate elements, and number of detected clickables and states. We also measure the generation performance which is the period taken to generate the static HTML pages from the inferred state-flow graph. G4. To test the capability of our method in crawling real sites and coping with unknown environments, we run Crawljax on four external cases C3– C6. We run Crawljax with depth level 2 on C3 and C5 each having a huge state space to examine the scalability of our approach in analyzing tens of thousands of candidate clickables and finding clickables. 5.5.3 Results and Evaluation Table 5.2 presents the results obtained by running Crawljax on the subject systems. The measurements were all read from the log file produced by Crawljax at the end of each process. G1. As can be seen in Table 5.2, for C1 Crawljax finds all the 16 expected clickables and states with a precision and recall of 100%. For C2, 33 elements were detected from the annotated 36. One explanation behind this difference could be the way some items are shown to the user in PetStore. PetStore uses a Catalog Browser to show a set of the total number of the product items. The 3 missing product items could be the ones that were Chapter 5. Crawling Ajax by Inferring User Interface State Changes 121
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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
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To the best of our knowledge, at th
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Table 1.1 ❳❳ ❳ ❳ ❳❳ Cha
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equally and we chose to use an alph
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A Component- and Push-based Archite
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Page Internet Application aRchitect
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XML message containing directives t
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server sends the data to the client
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• Code reuse: shared implementati
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Rest provides Ajax demands Large-gr
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work well, will lose customers (Off
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communication between client and se
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GWT uses an RPC style of calling se
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DOM Client Browser event update Aja
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Client Server Legend Interaction po
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User Interactivity User-perceived L
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User Interactivity User-perceived L
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and does not comply with the Resour
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2.8.9 Design Models Figure 2.8 show
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of user tasks as first class entiti
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Migrating Multi-page Web Applicatio
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Web App 1 Page deltaChange viewChan
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gational and structural model of th
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Classic Web application Retrieve pa
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Algorithm 1 1: procedure start (Pag
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3.5.2 Identifying Elements The list
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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
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://
Page 131: The generator uses JTidy 10 to pret
Page 135 and 136: 19247 examined candidate elements,
Page 137 and 138: ich interactivity and responsivenes
Page 139 and 140: 5.8 Related Work The concept behind
Page 141 and 142: Invariant-Based Automatic Testing o
Page 143 and 144: has some support for client-side sc
Page 145 and 146: 6.3.2 Trigger Once we are able to d
Page 147 and 148: a new edge is created on the graph
Page 149 and 150: Other Invariants. In line with the
Page 151 and 152: 6.7.1 Oracle Comparators After each
Page 153 and 154: Algorithm 4 Pre/postCrawling hooks
Page 155 and 156: LOC Server-side LOC Client-side DOM
Page 157 and 158: todo items, removing all items inst
Page 159 and 160: Failure Cause Violated Invariant In
Page 161 and 162: plications) is that it is very hard
Page 163 and 164: 1. A series of fault models that ca
Page 165 and 166: Conclusion ith the advent of Ajax t
Page 167 and 168: out to be an appropriate framework
Page 169 and 170: plays a more prominent role in the
Page 171 and 172: correct behavior. Violations in the
Page 173 and 174: A great deal of our work has focuse
Page 175 and 176: A Single-page Ajax Example Applicat
Page 177 and 178: 1 if(navigator.appName == "Microsof
Page 179 and 180: Figure A.5 The run-time DOM instanc
Page 181 and 182: 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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