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

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Figure 4.7 Sequence of Events in the Experimental Environment. uses Java’s new IO package (NIO). The NIO package follows the event-driven design, which allows the processing of each task as a finite state machine (FSM). As the number of tasks reach a certain limit, the excess tasks are absorbed in the server’s event queue. The throughput remains constant and the latency shows a linear increase. The Event-driven design is supposed to perform significantly better than thread-concurrency model (Welsh et al., 2001; Welsh and Culler, 2003). Jetty also contains a mechanism called Continuations (Jetty, 2006). This mechanism allows an HTTP request to be suspended and restarted after a timeout or after an asynchronous event has occurred. This way less threads are occupied on the server. Note that no other process was running in this application server other than TCPDump and UNIX top. Their load on the server and their effect on the results are negligible, since we only measure the PID of Jetty. Besides, as mentioned, the test machine has 2x Dual Core processors, and the server never had 100% load, which can be seen in Section 4.6.2. The connectivity between the server and DAS3 nodes is through a 100 Mbps ethernet connection. 4.5.3 Example Scenario Figure 4.7 shows the sequence of events of an example test scenario from the perspective of the servers: 1. The Service Provider publishes the stock data to the application server via an HTTP POST request, in which the creation date, the stock ID, and the stock message are specified. 2. For push: The application server pushes the data to all the subscribers of that particular stock. For pull: the application server updates the 90 4.5. Distributed Testing
Figure 4.8 Sample Stock Ticker Application internal stock object, so that when clients send pull requests, they get the latest data. 3. Each simulated client logs the responses (after some calculation) and sends it to the statistics server. 4.5.4 Sample Application: Stock Ticker We have developed a Stock Ticker web application as depicted in Figure 4.8. As new stock messages come in, the fields are updated in the browser accordingly. As mentioned before, the Stock Ticker has been implemented in three variants, namely, Cometd, DWR, and pull. The Cometd version. consists of a JSP page which uses Dojo’s Cometd library to subscribe to a channel on the server and receive the Stock data. For the server side, we developed a Java Servlet (PushServlet) that pushes the new data into the clients’ browsers using the Cometd library. The DWR version. consists of the same JSP page as the Cometd version, but uses the DWR library instead of Dojo on the client side. For the server side, we again have a separate Java Servlet (PushServlet) that pushes the data into the browsers, but this time it uses the DWR’s Comet servlet. The pull version. also consists of a JSP page, but instead of Cometd or DWR, it uses the normal bind method of Dojo to request data from the server. The pull nature is set using the standard setInterval JavaScript method. The interval at which the client should request/pull for new updates is configurable. On the server, a PullServlet is created which updates and keeps an internal stock object (the most recent one) and simply handles and responds to every incoming request the classical way. The Service Provider. uses the HTTPClient library 14 to publish stock data to the Servlets. The number of publish messages as well as the interval at which the messages are published are configurable. 14 http://jakarta.apache.org/commons/httpclient/ Chapter 4. Performance Testing of Data Delivery Techniques for Ajax 91
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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
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 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: 8. Start the publisher and begin pu
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 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
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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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