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

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Algorithm 5 InCrawling hook while deriving Ajax states 1: procedure GenerateEvent (State cs, Clickable c) 2: robot.fireEvent(c) 3: dom ← browser.getDom() 4: if distance(cs.getDom(), dom) > τ then 5: xe ← getXpathExpr(c) 6: ns ← State(dom) 7: sm.addState(ns) 8: sm.addEdge(cs, ns, Event(c, xe)) 9: sm.changeState(ns) 10: inCrawlingPlugins(ns) 11: crawl(cs) 12: sm.changeState(cs) 13: if browser.history.canBack then 14: browser.history.goBack() 15: else 16: browser.reload() 17: List E ← sm.getPathTo(cs) 18: for e ∈ E do 19: robot.fireEvent(e) 20: end for 21: end if 22: end if 23: end procedure 6.9 Empirical Evaluation In order to assess the usefulness of our approach in supporting modern web application testing, we have conducted a number of case studies, set up following Yin’s guidelines (Yin, 2003). Goal and Research Questions. Our goal in this experiment is to evaluate the fault revealing capabilities, scalability, required manual effort and level of automation of our approach. Our research questions can be summarized as: RQ1 What is the fault revealing capability of Atusa? RQ2 How well does Atusa perform? Is it scalable? RQ3 What is the automation level when using Atusa and how much manual effort is involved in the testing process? 6.9.1 Study 1: TUDU Our first experimental subject is the Ajax-based open source TUDU 9 web application for managing personal todo lists, which has also been used by other 9 http://tudu.sourceforge.net 142 6.9. Empirical Evaluation
LOC Server-side LOC Client-side DOM string size Candidate Clickables Detected Clickables Detected States Detected Entry Points DOM Violations Back-button Generated Test Cases Coverage Server-side Coverage Client-side Detected Faults Manual Effort Performance 3k 11k (ext) 24908 332 42 34 4 forms 182 false 32 73% 35% (ext) 80% 26.5 5.6 580 (int) (byte) 21 inputs 75% (int) (minutes) (minutes) Table 6.1 TUDU case study. Chapter 6. Invariant-Based Automatic Testing of Ajax User Interfaces 143
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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
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 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: Algorithm 4 Pre/postCrawling hooks
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
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
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A. Dijkstra. Stepping through Haske
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M. A. Abam. New Data Structures and
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