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

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S_1 S_4 Index S_2 S_3 Figure 5.2 The state-flow graph visualization. 5.3.3 Inferring the State Machine The state-flow graph is created incrementally. Initially, it only contains the root state and new states are created and added as the application is crawled and state changes are analyzed. The following components, also shown in Figure 5.3 participate in the construction of the state flow graph: • Embedded Browser: Our approach is based on an embedded browser interface (with different implementations: IE, Mozilla) capable of executing JavaScript and the supporting technologies required by Ajax (e.g., CSS, DOM, XMLHttpRequest). • Robot: A robot is used to simulate user input (e.g., click, mouseOver, text input) on the embedded browser. • Controller: The controller has access to the embedded browser’s DOM and analyzes and detects state changes. It also controls the Robot’s actions and is responsible for updating the State Machine when relevant changes occur on the DOM. After the crawling process is over, the controller also calls the Sitemap and Mirror site generator processes. • Finite State Machine: The finite state machine is a data component maintaining the state-flow graph, as well as a pointer to the current state. The algorithm used by these components to actually infer the state machine is shown in Algorithm 3. The start procedure (lines 1-8) takes care of initializing the various components and processes involved. The actual, recursive, crawling procedure starts at line 10: the main steps are explained below. 5.3.4 Detecting Clickables There is no direct way of obtaining all clickable elements in a DOM-tree, due to the reasons explained in Section 5.2. Therefore, our algorithm makes use of a set of candidate elements, which are all exposed to an event type (e.g., click, mouseOver). We use the click event type to present our algorithm, note, however, that other event types can be used just as well to analyze the effects on the DOM in the same manner. We distinguish three ways of obtaining the candidate elements: 112 5.3. A Method for Crawling Ajax
Robot click UI Browser generate click update event Crawljax Controller DOM event update Ajax Engine generate sitemap update State Machine generate mirror Inferring the State Machine Generating Indexable Pages Legend Sitemap Generator Sitemap XML Mirror site Generator Multi-page HTML link up Linker transform DOM to HTML Transformer Access Control flow Event invocation Data component Processing component Static file Output Figure 5.3 Processing view of the crawling architecture. • In a Full Auto Scan mode, the candidate clickables are labeled as such based on their HTML tag element name. For example, all elements with a tag div, a, span, input are considered as candidate clickable. Tag element selection can also be constrained by the attributes (using wildcards). This is the mode that is displayed in Algorithm 3. • In the annotation mode, we allow the HTML elements to have an attribute crawljax="true". This gives users the opportunity to explicitly mark certain elements as to be crawled, or elements to be excluded from the process by setting the attribute to false. Note that this mode requires access to the source code of the application for applying the annotations. • In the configured mode, we allow a user to specify by means of a domainspecific language which elements should be clicked (explained in more detail in Section 5.3.8). This allows the most precise control over the actual elements to be clicked. Note that, if desirable, these modes can be combined. After the candidate elements have been found, the algorithm proceeds to determine whether these elements are indeed clickable. For each candidate element, the crawler instructs the robot to execute a click (line 15) on the element (or other event types, e.g., mouseOver), in the browser. Chapter 5. Crawling Ajax by Inferring User Interface State Changes 113
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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
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 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: 5.3 A Method for Crawling Ajax The
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
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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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Analysis and Testing of Ajax-based Single-page Web Applications

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