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advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper describes the results of a process whereby the detection capability of 5 code replication detection tools for large software application are evaluated. Specifically this work focuses on the benefits of identification for preventative maintenance that is with the aim to remove some of the identified clones from the source code. A number of requirements are therefore identified upon which the tools are evaluated. The results of the analysis processes show that each tool has its own strengths and weaknesses and no single tool is able to identify all clones within the code. The paper proposes that it may be possible to use a combination of tools to perform the analysis process providing that adequate means of efficiently identifying false matches is found.

Case study is an important research methodology for software engineering. We have identified the need for checklists supporting researchers and reviewers in conducting and reviewing case studies. We derived checklists for researchers and reviewers respectively, using systematic qualitative procedures. Based on nine sources on case studies, checklists are derived and validated, and hereby presented for further use and improvement.

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