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

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Robot event UI Embedded Browser generate event update event Crawljax Controller DOM event update Ajax Engine Analyze Dom DOM Analyzer update State Machine Legend Access Control flow Event invocation Data component Processing component inCrawling plugins postCrawling plugins Static file Implementation DOM Validator Test-case Generator Test Cases Transform DOM to HTML report DB report Test Executor Validate HTML Validation Report Test Report Coverage Report Figure 6.3 Processing view of ATUSA. inCrawling occurs after each detected state change, different types of invariants can be checked through plugins such as Validated DOM, Consistent Back-button, and No Error Messages in DOM. postCrawling occurs after the crawling process is done and the state-flow graph is inferred fully. The graph can be used, for instance, in a plugin to generate test cases from. Algorithms 4 and 5 show the hooks along the crawling process. For each phase, Atusa provides the tester with specific APIs to implement plugins for validation and fault detection. Atusa offers generic invariant checking components, a plugin-mechanism to add application-specific state validators, and generation of a test suite from the inferred state-flow graph. Figure 6.3 depicts the processing view of Atusa, showing only the DOM Validator and Test Case Generator as examples of possible plugin implementations. Atusa supports looking for many different types of faults in Ajax-based applications, from errors in the DOM instance, to errors that involve the nav- 140 6.8. Tool Implementation: Atusa
Algorithm 4 Pre/postCrawling hooks 1: procedure Start (url, Set tags) 2: browser ← initEmbeddedBrowser(url) 3: robot ← initRobot() 4: sm ← initStateMachine() 5: preCrawlingPlugins(browser) 6: crawl(null) 7: postCrawlingPlugins(sm) 8: end procedure 9: procedure Crawl (State ps) 10: cs ← sm.getCurrentState() 11: ∆update ← diff(ps, cs) 12: analyseForms(∆update) 13: Set C ← getCandidateClickables(∆update, tags) 14: for c ∈ C do 15: generateEvent(cs, c) 16: end for 17: end procedure 18: procedure AnalyseForms (State cs) 19: for f orm ∈ cs.getForms() do 20: id ← getHashCode(form) 21: dbForm ← database.getForm(id) 22: if dbForm == null then 23: extractInsertForm(form, id) 24: else 25: fillFormInDom(browser, dbForm) 26: generateEvent(cs, dbForm.getSubmit()) 27: end if 28: end for 29: end procedure igational path, e.g., constraints on the length of the deepest paths (Benedikt et al., 2002), or number of clicks to a certain state. Whenever a fault is detected, the error report along the causing execution path is saved in the database so that it can be reproduced later easily. Implementation. Atusa is implemented in Java 1.6. The state-flow graph is based on the JGrapht library. The implementation details of the crawler can be found in Chapter 5. The plugin architecture is implemented through the Java Plugin Framework (JPF) 7 and we use Hibernate, a persistence and query service, to store the data in the database. Apache Velocity 8 templates assist us in the code generation process of JUnit test cases. 7 http://jpf.sourceforge.net 8 http://velocity.apache.org Chapter 6. Invariant-Based Automatic Testing of Ajax User Interfaces 141
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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
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 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: 6.7.1 Oracle Comparators After each
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 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.,
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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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