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Delivering a software product on time, within budget, and to an agreed level of quality is a critical concern for many software organizations. Underestimating software costs can have detrimental effects on the quality of the delivered software and thus on a company's business reputation and competitiveness. On the other hand, overestimation of software cost can result in missed opportunities to funds in other projects. In response to industry demand, a myriad of estimation techniques has been proposed during the last three decades. In order to assess the suitability of a technique from a diverse selection, its performance and relative merits must be compared. The current study replicates a comprehensive comparison of common estimation techniques within different organizational contexts, using data from the European Space Agency. Our study is motivated by the challenge to assess the feasibility of using multi-organization data to build cost models and the benefits gained from companysp...

Traditionally, researchers have used either off-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software effort estimates. More recently, attention has turned to a variety of machine learning methods such as artificial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software effort prediction systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the 1 Chris Schofield is now with Nortel (cscho@nortelnetworks.com). --- 2 --- prediction systems in terms of three factors: accuracy, explanatory value and configurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by pr...

Abstract. As a contribution to the recent debate on simple implementations of dictionaries, we present new maintenance algorithms for balanced trees. In terms of code simplicity, our algorithms compare favourably with those for deterministic and probabilistic skip lists.

An exploratory study was conducted (a) to examine whether experieticed software managers could generate accurate estimates of effort required for proposed software projects and (b) to document the strategies they bring to bear in their estimations. Five experienced software project managers served as expert subjects for the study. Each manager was first asked to sort a set of 37 commonly-used estimation parameters according to Ibe importance of their effect on effort estimation. Once this task was completed, the manager was then presented with data from ten actual software projects, one at a time, and asked to estimate the effort (in worker-months) required to complete the projects. The project sizes ranged from 39,000 to 450,000 lines of code and varied from 23 to 1,107 worker-months to complete. All managers were tested individually. The results were compared to those of two popular analytical models—Function Points and COCOMO. Results show that the managers made more accurate estimates than the uncalibrated analytical models. Additionally, a process-tracing analysis revealed that the managers used two

In this paper, we showed how to quantify the quality of IT forecasts based on Boehm’s cone of uncertainty and DeMarco’s Estimating Quality Factor. With these, we support decision making by providing critical information on IT forecasting quality to IT governors. We illustrated that plotting forecast to actual ratios against a predefined referential conical shape reveals potential biases, for instance political, involved with IT forecasting. The Estimating Quality Factor quantifies the deviation of forecasts from their actual value. Using simulations, we showed that the conical shape of Boehm’s cone is not caused by improved estimation, but can also be found when estimation accuracy decreases. We illustrated our approach by applying it to four real-world case studies (1741 projects, 12187 forecasts, 1059 million Euro). Finally, we surveyed benchmarks related to forecasting and proposed new benchmarks based on our extensive data. Most forecasting benchmarks in the literature turned out to have an unknown bias. As a consequence, we argued that such figures including Standish’s project success benchmarks are meaningless.

Maintenance costs of Information Systems (ISs) are high and are growing. Part of the costs come from low software quality. The quality of software can be measured when it is maintained. By improving the quality of maintenance prone modules future maintenance costs may be lowered for those modules. In this article we will discuss how to measure software quality and we will also present a prototype of a software quality metrics database that has been set up to especially focus on software maintenance cost information. Keywords Software quality, measurement tool for IS management, software metrics, metrics database 1 Introduction The fact is that most of the yearly software budget is normally spent on maintenance of existing information systems [Art88, Boe81, LS80]. Systems of poor quality demand more maintenance resources than systems of good quality. Development of new systems without strict requirement for quality control supported by quality measuring will lead to even bigger needs ...

Effort estimation by analogy (EBA) is an established method for software effort estimation. For this paper, we understand EBA as a meta-method which needs to be instantiated and customized at different stages and decision points regarding a specific context. Some example decision problems are related to the selection of the similarity measures, the selection of analogs for adaptation or the weighting and selection of attributes. This paper proposes a decision-centric process model for EBA by generalizing the existing EBA methods. Typical decision-making problems are identified at different stages of the process as part of the model. Some existing solution alternatives of the decision-making problems are then studied. The results of the decision support analysis can be used for better understanding of EBA related techniques and for providing guidelines for implementation and customization of general EBA. An example case of the process model is finally presented. 1.

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Maintenance costs of Information Systems (ISs) are high today and are growing. Part of the costs come from low software quality. The software quality can be measured when it is maintained. By improving the quality of maintenance prone modules future maintenance costs may be curtailed. In this article we will discuss how to measure software quality and we will also present a prototype that has been built to especially focus on the software quality factor maintainability. The fundamentals of measurement is discussed and also di#erent software quality constructs found in the litterature. Keywords Software quality, measurement tool for IS management, traditional software metrics, data collection 1 Introduction Most of the yearly software budget is normally spent on maintenance of old information systems [Art88,Boe81,LS80]. Systems of poor quality demand more maintenance resources than systems of good quality. Development of new systems without strict requirement for quality control suppo...