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

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applied the transitive property of clone pairs. For this reason, it is possible that the algorithm finds larger clusters than expected. Although Chapter 3 proposed a complete migration approach, it specifically focused on the first part, namely reverse engineering an abstract model of the source multi-page interface for the purpose of migration towards a component-based single-page interface. Automating a migration process is a daunting task, still very ad hoc, and due to the complexity of web systems, full automation is probably not achievable. Nevertheless, reverse engineering techniques, implemented in a tool called Retjax, proved to be a great vehicle in automating parts of the process of program comprehension and inferring abstract models from the source system. The applicability of the proposed approach is currently limited to web applications with simple user interfaces. A limitation of the approach is currently the static analysis technique adopted to retrieve client pages from the source system. In fact, Retjax navigates anchors/links that are found in the HTML code of navigated pages. However, a URL link, could generate different client pages at multiple requests from the server. The retrieval phase of Retjax is, to certain extent, similar to what Crawljax does, i.e., inferring an abstract model of the web application. The edit distance method used is also the same in both approaches. The difference lies in the fact that Retjax conducts a static analysis of the HTML code to detect hyperlinks, while Crawljax performs a dynamic analysis of all possible clickable elements to find state changes. It is thus possible to augment Retjax with the capabilities of Crawljax to retrieve a more complete navigational model from the target system. The second part of the migration process, namely single-page model definition and target UI model transformation, is missing in this thesis. We have, however, initiated research (Gharavi et al., 2008) to adopt a model-driven engineering approach to Ajax, which could be integrated in the migration process. Retjax is geared towards classic multi-page web applications and although the intention is to use the resulting candidate components for migration towards Ajax, the tool itself is not Ajax specific. In fact, Retjax can be used as a clustering tool on any standards-based web application. The schemabased clustering approach has various applications in cases where web page comparison on an structural level plays an important role. Research Question 3 What are the challenges for analyzing and testing Ajax applications in an automatic approach? Ajax applications have a number of characteristics that make them challenging for automatic analysis and testing. First of all, like classical web systems, Ajax applications have a distributed client/server nature, and testing distributed systems is known to be very demanding. Ajax settings do, however, pose a greater challenge than conventional web settings, since the client 156 7.2. Research Questions Revisited
plays a more prominent role in the client/server distributed spectrum, as more logic is being ported from the server-side to the client-side. In addition, unlike traditional object-oriented systems, the heterogeneous nature of web applications increases the difficulty of useful analysis based on inspections of the source code. Static analysis techniques, which are commonly used with success on object-oriented applications, have serious limitations in analyzing modern Ajax applications where understanding the runtime behavior is crucial. As we have seen in Chapter 5, even simply finding and following links is not possible any longer on Ajax interfaces by static analysis of the source code. Using dynamic analysis to gather data from a running web program seems then the right direction to take, which in turn has its own difficulties and limitations, such as incompleteness and scalability (Cornelissen et al., 2009). Research Question 3.1 What are the tradeoffs of applying pull- and push-based data delivery techniques on the web? Can we set up an automated distributed test environment to obtain empirical data for comparison? Conducting an experiment to compare the actual tradeoffs of pull and push based web data delivery techniques turned out to be a difficult task, as explained in Chapter 4. Our first attempt in conducting the experiment consisted of a great deal of manual work, from starting the servers, the client simulation processes, to gathering and saving the run-time information, parsing the data, and generating graphs. The difficulty in coordination of the different modules in such a distributed environment encouraged us to opt for an automated approach, implemented in Chiron. Besides decreasing the manual effort, such an automated testing environment greatly increases the accuracy of the experiment, since similar test runs can be sequentially performed with exactly the same parameters. The proposed experiments are very complex, since the execution environment contains various distributed interacting concurrent software processes and distributed hardware devices. For these reasons, the execution of our experiments is subject to validation threats, as discussed thoroughly in Chapter 4. Although the experiment in Chapter 4 has been conducted on one sample application, and the Comet push servers are still experimental, the results show a promising trend in web data delivery techniques which clearly out perform pull-based approaches in terms of data coherence and network performance. Currently, scalability of the server, when a high number of clients has to be served, is the main concern when push is used. Chiron is not bound to any specific web technology and almost every aspect of the tool is configurable, and since it has been made open source, similar experiments by others can be carried out to dynamically analyze the runtime performance properties of web systems. Chapter 7. Conclusion 157
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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
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 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: out to be an appropriate framework
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
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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