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

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similarity namely, structure, content, and scripting code. In (De Lucia et al., 2004a), the authors apply the techniques in a re-engineering case study. WANDA (Antoniol et al., 2004) is a tool for dynamic analysis of web applications. It instruments web pages and collects information during the execution. This information is used to extract diagrams, such as component, deployment, sequence and class diagrams according to Conallen UML extensions. Cordy et al. (2004) use an island grammar to identify syntactic constructs in HTML pages. The extracted constructs are then pretty-printed to isolate potential differences between clones to as few lines as possible and compared to find candidate clones using the UNIX diff tool. A study of cloning in 17 web applications is presented by Rajapakse and Jarzabek (2005), aiming at understanding the nature of web clones and their sources. Lanubile and Mallardo (2003) discuss a pattern matching algorithm to compare scripting code fragments in HTML pages. Stroulia et al. (2003) analyze traces of the system-user interaction to model the behavior of the user interface for migrating the user interface from a legacy application to a web-based one. GUI Ripping (Memon et al., 2003) creates a model from a graphical user interface for testing purposes, i.e., it generates test cases to detect abnormalities in user interfaces. Vanderdonckt et al. (2001) propose Vaquista, a XIML-based tool for static analysis of HTML pages. Its goal is to reverse engineer the user interface model from individual HTML pages to make them device independent. Our classification approach is in two ways different from work conducted earlier on this topic. First, while others have based their structural similarity notion on the edit distance calculated on the syntax trees of pages, we propose a meta-model similarity notion and implement a schema-based clustering approach which, in the case of HTML pages, provides very promising results. Second, we try to find the clusters along the navigational path (different levels), as opposed to classifying all pages at once (one level) in order to identify candidate UI components along with their navigational model. 3.10 Concluding Remarks In this chapter, we emphasized the rise of single-page Ajax applications and the need for support in migrating classical multi-page web applications to this new paradigm. The main contributions of this chapter can be summarized as follows. First, we proposed a migration process, consisting of five steps: retrieving pages, navigational path extraction, user interface component model identification, single-page user interface model definition, and target model transformation. The second and third steps were described in full detail. Second, we introduced a novel meta-model similarity metric for web page classification, which in our case studies achieves a higher recall and precision than approaches based directly on the HTML syntax trees. 72 3.10. Concluding Remarks
Third, we proposed a schema-based clustering technique that operates per navigational level, instead of on the full set of web pages. Furthermore, we provide a mechanism for simplifying navigational paths, allowing us to find candidate user interface components through a differencing mechanism. Future work encompasses the in-depth application of our approach in other case studies and more focus on the last two steps of the proposed migration process and study how a model-driven approach can be adopted in Ajax development. Furthermore, we will investigate how we can take advantage of dynamic analysis concepts to support the retrieving pages step of the migration process. Finally, more research is needed to understand to what extent the serverside code should be adapted while migrating from a multi-page web application to a single-page Ajax interface. Chapter 3. Migrating Multi-page Web Applications to Ajax Interfaces 73
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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
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
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: target system. Even though the tech
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 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
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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
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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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