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

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params a string representing the request content. Used with the POST method to send form parameters to the server. target element id the ID of the DOM element where the response content should be injected into. callbackFunction handles the response content. Using these parameters, the loadFragment function sends a request to the server and calls the callback function when a valid response is obtained from the server. The getData function is a wrapper function which calls the loadFragment function with the correct parameters. The getData function is attached to the onclick attribute of the clickable element, as can be seen in line 12 of Figure A.1. This function is called with two parameters, namely server.php as the URL, and container as the target element ID. This gives us the desired behavior that when the element is clicked by the user, the getData method is called, which in turn calls the loadFragment function to get data from the server.php URL on the server. A.4 The Server-side Code Figure A.4 show the contents of our server.php which simply returns the code for a HTML form. 1 Figure A.4 server.php source code. A.5 DOM Injection Once the response of the server is successfully arrived at the browser, the callback function is called. The insertIntoDOMElement function takes the ID of the target element and the response data, as parameters. It uses the ID to find a reference to the corresponding run-time element in the DOM instance (line 28 in Figure A.3). Once a reference has been found, the response data is injected into the inner part of that element. As soon as the content is injected 166 A.4. The Server-side Code
Figure A.5 The run-time DOM instance after the new content is retrieved and injected into the DOM-tree (bottom) and the updated user interface (top). into the target DOM element (in this case the DIV element with ID content), the browser updates the user interface. This way a complete client/server round-trip can be made and the user interface of the browser can be updated in the background, without having to refresh the page. Figure A.5 depicts the updated DOM and browser user interface. Appendix A. A Single-page Ajax Example Application 167
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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
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Figure 3.7 A candidate UI component
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target system. Even though the tech
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Third, we proposed a schema-based c
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Performance Testing of Data Deliver
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Figure 4.1 A screenshot taken from
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the user. Ideally, the pulling inte
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Streaming Figure 4.3 shows how the
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4.4 Experimental Design In this sec
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Mean Publish Trip-time (MPT) We def
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tributes to the complexity of contr
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8. Start the publisher and begin pu
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Figure 4.8 Sample Stock Ticker Appl
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Figure 4.9 Mean Publish Trip-time C
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4.6.3 Received Publish Messages Fig
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Figure 4.12 Mean Number of Received
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Figure 4.13 Percentage of data item
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with pull is higher than push. This
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this scenario, a data item will onl
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With the pull approach, achieving t
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Crawling Ajax by Inferring User Int
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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
Page 127 and 128: 5.3.5 Creating States After firing
Page 129 and 130: 1 crawl MyAjaxSite { 2 url: http://
Page 131 and 132: The generator uses JTidy 10 to pret
Page 133 and 134: G1. For the experiment we have manu
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: 1 if(navigator.appName == "Microsof
Page 181 and 182: Figure A.7 The request/response tra
Page 183 and 184: Samenvatting ⋆ Analyse en Testing
Page 185 and 186: een dergelijk model te creëren, do
Page 187 and 188: Bibliography Abrams, M., Phanouriou
Page 189 and 190: Bouras, C. and Konidaris, A. (2005)
Page 191 and 192: De Lucia, A., Scanniello, G., and T
Page 193 and 194: Garofalakis, M., Gionis, A., Rastog
Page 195 and 196: Levenshtein, V. L. (1996). Binary c
Page 197 and 198: Netscape (1995). An exploration of
Page 199 and 200: Sprenkle, S., Pollock, L., Esquivel
Page 201 and 202: Wohlin, C., Runeson, P., Höst, M.,
Page 203 and 204: Curriculum Vitae Personal Data Full
Page 205 and 206: A. Dijkstra. Stepping through Haske
Page 207 and 208: M. A. Abam. New Data Structures and
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