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

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4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.7.1 The Research Questions Revisited . . . . . . . . . . . . . 98 4.7.2 Threats to Validity . . . . . . . . . . . . . . . . . . . . . . 101 4.8 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 104 5 Crawling Ajax by Inferring User Interface State Changes 107 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.2 Challenges of Crawling Ajax . . . . . . . . . . . . . . . . . . . . 109 5.2.1 Client-side Execution . . . . . . . . . . . . . . . . . . . . 109 5.2.2 State Changes & Navigation . . . . . . . . . . . . . . . . 109 5.2.3 Dynamic Document Object Model (DOM) . . . . . . . . 109 5.2.4 Delta-communication . . . . . . . . . . . . . . . . . . . . 110 5.2.5 Elements Changing the Internal State . . . . . . . . . . . 110 5.3 A Method for Crawling Ajax . . . . . . . . . . . . . . . . . . . . 111 5.3.1 User Interface States . . . . . . . . . . . . . . . . . . . . . 111 5.3.2 The State-flow Graph . . . . . . . . . . . . . . . . . . . . 111 5.3.3 Inferring the State Machine . . . . . . . . . . . . . . . . . 112 5.3.4 Detecting Clickables . . . . . . . . . . . . . . . . . . . . . 112 5.3.5 Creating States . . . . . . . . . . . . . . . . . . . . . . . . 115 5.3.6 Processing Document Tree Deltas . . . . . . . . . . . . . 115 5.3.7 Navigating the States . . . . . . . . . . . . . . . . . . . . 115 5.3.8 CASL: Crawling Ajax Specification Language . . . . . . 117 5.3.9 Generating Indexable Pages . . . . . . . . . . . . . . . . . 117 5.4 Tool Implementation: Crawljax . . . . . . . . . . . . . . . . . . 118 5.5 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5.5.1 Subject Systems . . . . . . . . . . . . . . . . . . . . . . . . 119 5.5.2 Experimental Design . . . . . . . . . . . . . . . . . . . . . 120 5.5.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . 121 5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 5.6.1 Back Implementation . . . . . . . . . . . . . . . . . . . . 123 5.6.2 Constantly Changing DOM . . . . . . . . . . . . . . . . . 123 5.6.3 Cookies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 5.6.4 State Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 5.7 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 5.7.1 Search Engines . . . . . . . . . . . . . . . . . . . . . . . . 124 5.7.2 Discoverability . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 5.8 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.9 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 127 6 Invariant-Based Automatic Testing of Ajax User Interfaces 129 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.3 Ajax Testing Challenges . . . . . . . . . . . . . . . . . . . . . . . 132 6.3.1 Reach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 vi Contents
6.3.2 Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.3.3 Propagate . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.4 Deriving Ajax States . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.5 Data Entry Points . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.6 Testing Ajax States Through Invariants . . . . . . . . . . . . . . 136 6.6.1 Generic DOM Invariants . . . . . . . . . . . . . . . . . . . 136 6.6.2 State Machine Invariants . . . . . . . . . . . . . . . . . . 137 6.6.3 Application-specific Invariants . . . . . . . . . . . . . . . 137 6.7 Testing Ajax Paths . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.7.1 Oracle Comparators . . . . . . . . . . . . . . . . . . . . . 139 6.7.2 Test-case Execution . . . . . . . . . . . . . . . . . . . . . . 139 6.8 Tool Implementation: Atusa . . . . . . . . . . . . . . . . . . . . 139 6.9 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 142 6.9.1 Study 1: TUDU . . . . . . . . . . . . . . . . . . . . . . . . 142 6.9.2 Study 2: Finding Real-Life Bugs . . . . . . . . . . . . . . 145 6.10 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 6.10.1 Automation Scope . . . . . . . . . . . . . . . . . . . . . . 148 6.10.2 Invariants . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 6.10.3 Generated versus hand-coded JavaScript . . . . . . . . 149 6.10.4 Manual Effort . . . . . . . . . . . . . . . . . . . . . . . . . 149 6.10.5 Performance and Scalability . . . . . . . . . . . . . . . . 149 6.10.6 Application Size . . . . . . . . . . . . . . . . . . . . . . . 149 6.10.7 Threats to Validity . . . . . . . . . . . . . . . . . . . . . . 150 6.10.8 Ajax Testing Strategies . . . . . . . . . . . . . . . . . . . . 150 6.11 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 150 7 Conclusion 153 7.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 7.2 Research Questions Revisited . . . . . . . . . . . . . . . . . . . . 154 7.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 7.4 Future Work and Recommendations . . . . . . . . . . . . . . . . 161 7.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 162 A A Single-page Ajax Example Application 163 A.1 The HTML Single-page . . . . . . . . . . . . . . . . . . . . . . . 163 A.2 The Run-time DOM . . . . . . . . . . . . . . . . . . . . . . . . . . 163 A.3 Requesting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 A.4 The Server-side Code . . . . . . . . . . . . . . . . . . . . . . . . . 166 A.5 DOM Injection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 A.6 Submitting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Samenvatting (Dutch Summary) 171 Bibliography 175 Curriculum Vitae 191 Contents vii
Page 1 and 2: Analysis and Testing of Ajax-based
Page 3 and 4: Contents Preface List of Acronyms i
Page 5: 3.3.4 Single-page UI Model Definiti
Page 9 and 10: Preface our years have passed since
Page 11 and 12: List of Acronyms ASP Active Server
Page 13 and 14: Introduction A ccording to recent s
Page 15 and 16: Figure 1.2 Screen shot of the Mosai
Page 17 and 18: Figure 1.4 Screen shot of OpenLaszl
Page 19 and 20: DOM APIs, a technique that was call
Page 21 and 22: Figure 1.6 Screen shot of Google Su
Page 23 and 24: Figure 1.7 Screen shot of Google Do
Page 25 and 26: Reengineering Analysis & Testing Ar
Page 27 and 28: client/server and network-based env
Page 29 and 30: To the best of our knowledge, at th
Page 31 and 32: Table 1.1 ❳❳ ❳ ❳ ❳❳ Cha
Page 33 and 34: equally and we chose to use an alph
Page 35 and 36: A Component- and Push-based Archite
Page 37 and 38: Page Internet Application aRchitect
Page 39 and 40: XML message containing directives t
Page 41 and 42: server sends the data to the client
Page 43 and 44: • Code reuse: shared implementati
Page 45 and 46: Rest provides Ajax demands Large-gr
Page 47 and 48: work well, will lose customers (Off
Page 49 and 50: 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
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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
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With the pull approach, achieving t
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Crawling Ajax by Inferring User Int
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discusses the findings and open iss
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5.3 A Method for Crawling Ajax The
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Robot click UI Browser generate cli
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5.3.5 Creating States After firing
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1 crawl MyAjaxSite { 2 url: http://
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The generator uses JTidy 10 to pret
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G1. For the experiment we have manu
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19247 examined candidate elements,
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ich interactivity and responsivenes
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5.8 Related Work The concept behind
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Invariant-Based Automatic Testing o
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has some support for client-side sc
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6.3.2 Trigger Once we are able to d
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a new edge is created on the graph
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Other Invariants. In line with the
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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
Page 197 and 198:
Netscape (1995). An exploration of
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Sprenkle, S., Pollock, L., Esquivel
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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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