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This chapter describes the kinds of test execution results you might want to collect, the ways you might want to consider analyzing these results, and the interpretations you may place on your analysis outcome The rst step in discussing test execution results is to assume that you have done a thorough job of test planning If you have unintentionally omitted certain application functions or business activities or structural components or performance aspects of your application from your test planning, then your test coverage will be inadequate One planning key to successful test results analysis is the clear de nition of success for each test case It is common for a test case to have a number of expected results If the actual results obtained from a test execution all match the expected results, then the test case is normally considered attempted and successful.

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Therefore, we t our model parameters according to the TR6 dataset Simulation Results Based upon our ndings in section 1513 we developed an ns-2 script to model a WoW-like client to sever connections The following paragraph discusses the introduction of two new parameters that adapt the measured values to inputs for the simulation In contrast to UDP, using a TCP connection between server and client introduces interaction at the protocol level Therefore, it was necessary to transform the input parameters obtained in the packet traces into suitable parameters to drive a simulation In other words we could not make direct use of the packet sizes measured, but had to derive a data unit from the view of the application We then tted these new parameters and implemented them into the ns-2 simulation environment.

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If only some of the actual results obtained from a test execution match the expected results, then the test case is normally considered attempted but unsuccessful A test case may be attempted but unsuccessful because the actual results do not match the expected results or because the software halted with an error message before the test case was completed Test cases that have not been executed are initially marked unattempted The incremental discovery and correction retesting of software defects is the primary way that software testers help software developers implement the development requirements The fewer the latent defects in the delivered software, the closer the software comes to ful lling the requirements The success of incremental defect discovery is directly related to the management process used to track defects from discovery to correction Successful incremental defect tracking can provide a third testing metric normally called the defect backlog.

Filtered Packet Trace Analysis In the previous paragraph we showed that there is a high fraction of ACK packets in the trace The rst straightforward approach was to lter all.

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If your testing has discovered 300 software defects and development has successfully corrected 100 of these defects, then a backlog of 200 defects still need to be corrected The challenge for the development team is.

PPE, http://ppewww.ph.gla.ac.uk/. EPCC, http://www.epcc.ed.ac.uk/. IPPP, http://www.ippp.dur.ac.uk/. OGSA-DAI Projects, http://www.ogsadai.org.uk/projects/. e-DiaMoND, http://www.ediamond.ox.ac.uk/. ODD-Genes, http://www.epcc.ed.ac.uk/ oddgenes/. SunDCG, http://www.epcc.ed.ac.uk/sungrid/. UCL Condor Pool, http://grid.ucl.ac.uk/Condor.html. eMinerals, http://eminerals.org/. Synopsys, http://www.synopsys.com. BMO, http://www.bmo.com. AXYZ, http://www.axyzfx.com. Zhao, Y., Wilde, M., Foster, I., Voeckler, J., Jordan, T., Quigg, E. and Dobson, J. Grid Middleware Services for Virtual Data Discovery, Composition, and Integration. Proceedings of ACM/IFIP/USENIX 5th International Middleware Conference, October 2004, Toronto, Canada. ACM. Foster, I., Voeckler, J., Wilde, M. and Zhao, Y. Chimera: A Virtual Data System for Representing, Querying, and Automating Data Derivation. Proceedings of the 14th Conference on Scienti c and Statistical Database Management, July 2002, Edinburgh, UK. The Quarknet Project, http://quarknet.fnal.gov. GENIUS, http://genius.ct.infn.it/. DataGrid, http://www.opensource.org/licenses/eudatagrid.php. EnginFrame, http://www.enginframe.com. ALICE, http://www.cern.ch/Alice. ATLAS, http://atlasexperiment.org/. CMS, http://cmsinfo.cern.ch/Welcome.html/. DataTAG, http://datatag.web.cern.ch/datatag/. WorldGrid, http://www.ivdgl.org/projinfo/. Discovery Net, http://www.discovery-on_the.net/. Rowe, A., Kalaitzopoulos, D., Osmond, M., Ghanem, M. and Guo, Y. The Discovery Net System for High Throughput Bioinformatics. Proceedings of the 11th International Conference on Intelligent Systems for Molecular Biology, July 2003, Brisbane, Australia. Curcin, V., Ghanem, M. and Guo, Y. SARS Analysis on the Grid. Proceedings of UK e-Science All Hands Meeting, September 2004, Nottingham, UK. Ghanem, M., Guo, Y., Hassard, J., Osmond, M. and Richards, R. Sensor Grids for Air Pollution Monitoring. Proceedings of UK e-Science All Hands Meeting, September 2004, Nottingham, UK. Liu, J.G. and Ma, J. Imageodesy on MPI & GRID for Co-seismic Shift Study Using Satellite Optical Imagery. Proceedings of UK e-Science All Hands Meeting, September 2004, Nottingham, UK. SC2002, www.sc-conference.org/sc2002. LeSC, http://www.lesc.ic.ac.uk/. GenBank, http://www.ncbi.nlm.nih.gov/Genbank/GenbankOverview.html. Landsat, http://www.landsat.org/. Stevens, R.D., Tipney, H.J., Wroe1, C.J., Oinn, T.M., Senger, M., Lord, P.W., Goble, C.A., Brass, A. and Tassabehji, M. Exploring Williams Beuren Syndrome Using myGrid. Bioinformatics, 20(Suppl. 1): i303 i310 (2003).

Specify correct kernel source directory: usr/src/linux-2.4.2/ (for Redhat 7.1 only) Specify correct pcmcia-cs source directory: /usr/src/linux-2.4.2/pcmcia-cs-3.1.29/ (for Redhat 7.1) Ensure that the PCMCIA card services are the only modules built (vice PCI/PLX services).

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