{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T06:32:29Z","timestamp":1712125949714},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2006,7,25]],"date-time":"2006-07-25T00:00:00Z","timestamp":1153785600000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Softw. Maint. Evol.: Res. Pract."],"published-print":{"date-parts":[[2006,7]]},"abstract":"Abstract<\/jats:title>Assessing progress and stability in rapid, highly iterative and agile object\u2010oriented software development processes is difficult because program evolution appears somewhat disorderly. With agile software development methods, the expected time to reach software stability is obscured by the dynamic nature of the agile process itself. The System Design Instability (SDI) metric was developed to help analyze object\u2010oriented software evolution under these conditions. In this paper we examine a new version of SDI, called SDIe<\/jats:sub>, based on the maximum entropy in a system. We believe our new measure is easier to calculate than the original version of SDI since it is based on data that are more commonly available than those used by SDI; also, SDIe<\/jats:sub> gives a more accurate indication of software stability and maturity since it suffers less from data spikes. To validate the new metric, we use maintenance data from a commercial software project, developed using an agile process; these are the same data that were collected and analyzed in the original SDI study. In addition, we apply the refined SDI metric to the evolution of a well\u2010established, open\u2010source, iteratively\u2010developed software product. The results of our case study indicate our new SDIe<\/jats:sub> metric is a viable measure of system design stability. 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