Continual Resource Estimation for Evolving Software

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Continual Resource Estimation for Evolving Software Juan F. Ramil Computing Dept., Faculty of Maths and Computing, The Open University Walton Hall, Milton Keynes, MK7 6AA, U.K. Tel. + 44 (0) 1908 654088 Fax + 44 (0) 1908 652140 [email protected] http://mcs.open.ac.uk/jfr46 Abstract A resource estimation approach, specifically oriented towards long-lived software being actively evolved, is proposed and investigated. The approach seeks to be coherent with empirically grounded knowledge including the observation that the software progresses through a series of stages during its lifetime and that a distinctive model characterises the economics of each individual stage. Instead of a single estimation model for the whole lifetime of the software, the approach calibrates models to each stage. Its feasibility was assessed in case studies using industrial data, yielding an accuracy (in magnitude of relative error – MRE) between 20 and 33 percent. 1. Introduction Resource estimation is critical in order to allocate an adequate level of resources for software engineering activities. Appropriate resource estimation approaches can help prevent under or over, that is sub-optimal, staffing and its consequences. The term software evolution encompasses all activities required after the first operational release of the software has been delivered in order to implement new requirements and keep the software compatible with a changing operational domain [4]. Evolution is, essentially, a continual activity which encompasses what in general has been termed maintenance. Surveys suggest that typically the largest portion of human resources applied over the lifetime of a software system is devoted to evolution, not to ab initio development [6]. The majority of empirical estimation approaches for maintenance are extrapolations of approaches conceived with ab initio development in mind. However, there are important differences between ab initio development and long term evolution that make the existing estimation approaches largely inappropriate for the continual evolution situation. There is a need for resource estimation approaches that address long-lived software under continual evolution. Traditionally, model-based (also called algorithmic) resource estimation for software has been seen in the literature mainly as a technical problem: given sufficient data and the proper modelling skills, an appropriate estimation model will be derived. In general, however, the estimation problem is far from being adequately solved, even less so in the context of software evolution. Each estimation approach contains built-in assumptions. If those assumptions are (or become) incompatible with the domain within the approach is actually applied, the approach is unlikely to be accurate, even if it provided good results in, for example, local studies with (limited) data possibly reflecting a single or a reduced set of domains. On the contrary, if the assumptions of the approach are shown to be compatible with the domain of application and, in addition, a local
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