Legal documents play a basic role in dischargingrnthe law to the public, besides constitutingrnlearning material for students, researchers andrnlegal practitioners. Several web portals andrndocument repositories are hosting legalrndocuments. Legal documents contain text richrncontents that can be structured and marked withrndescription languages such as XML. This basicrnfeature can be exploited by XML based retrievalrnmodels to return legal document parts as a matchrnfor information needs rather than presenting thernentire statue. The main principle behind the XMLrnbased retrieval models is to take the structuredrninformation of documents (as formatted by XMLrntags) and the hierarchical document organizationrninto account when responding to user queries andrnwhen computing the relevance ranking.rnUnder different paradigms, legal documents arernsubjected to change, as the laws they carryrnundergo modifications or amendments. Therndynamicity of the law is manifested by thernchange in the legal documents, and this change inrnturn will have a sever consequence on thernretrieval quality of XML based legal documentrnretrieval systems. In this work we present arngeneric model for a change-aware legal documentrnretrieval system using XML based IR approach.rnBased on the model designed we have developedrna prototype system that demonstrates the validityrnof our ideas and algorithms. By identifying thernstatus of legal contents and by tracking theirrnrelationship, we have achieved an improvedrnaccess to legislative contents. In order to realizernthis approach we have incorporated the Lucenernsearching and indexing APIs and proposed arnnovel re-ranking algorithm that considers thernchanges in the legal document contents and statusrnof the law contained.rnThe evaluation of our system shows a significantrnturnover as the result of incorporating the idea ofrnre-ranking search results. Before re-ranking thernaverage precision value for the first five resultsrnreturned was 46% but after re-ranking thernaverage precision becomes 82.5% in satisfyingrnuser information needs.