Due to their pivotal role in software engineering, considerable effort is spent on the quality assurance of software requirements specifications. As they are mainly described in natural language, relatively few means of automated quality assessment exist. However, we found that clone detection, a technique widely applied to source code, is promising to assess one important quality aspect in an automated way, namely redundancy that stems from copy & paste operations. This paper describes a large-scale case study that applied clone detection to 28 requirements specifications with a total of 8,667 pages. We report on the amount of redundancy found in real-world specifications, discuss its nature as well as its consequences and evaluate in how far existing code clone detection approaches can be applied to assess the quality of requirements specifications in practice.
Student plagiarism is an ever-increasing problem for academic institutions. A growing number of students are using material from the Web in their submissions, without properly acknowledging the source. This paper reviews the need for widespread plagiarism detection systems and evaluates available Web based detection services. Four services are discussed: the Measure of Software Similarity (MOSS) service for program source code and the plagiarism.org, Integriguard and copycatch.com services for free-text submissions. The downloadable Essay Verification Engine (EVE) tool for free-text detection of Web plagiarism is also evaluated. The paper finds that all five could be invaluable resources for academic institutions as they strive for a pro-active anti-plagiarism policy. The paper concludes by looking at the authors' current work to combat plagiarism.  In order to increase retrieval precision, some new search engines provide manually verified answers to Frequently Asked Queries (FAQs). An underlying task is the identification of FAQs. This paper describes our attempt to cluster similar queries according to their contents as well as user logs. Our preliminary results show that the resulting clusters provide useful information for FAQ identification.