Experimental Study Using Functional Size Measurement in Building Estimation Models for Software Project Size

This paper reports on an experiment that investigates the predictability of software project size from software product size. The predictability research problem is analyzed at the stage of early requirements by accounting the size of functional requirements as well as the size of non-functional requirements. The experiment was carried out with 55 graduate students in Computer Science from Concordia University in Canada. In the experiment, a functional size measure and a project size measure were used in building estimation models for sets of web application development projects. The results show that project size is predictable from product size. Further replications of the experiment are, however, planed to obtain more results to confirm or disconfirm our claim

Preliminary Results in a Multi-site Empirical Study on Cross-organizational ERP Size and Effort Estimation

This paper reports on initial findings in an empirical study carried out with representatives of two ERP vendors, six ERP adopting organizations, four ERP implementation consulting companies, and two ERP research and advisory services firms. Our study’s goal was to gain understanding of the state-of-the practice in size and effort estimation of cross-organizational ERP projects. Based on key size and effort estimation challenges identified in a previously published literature survey, we explored some difficulties, fallacies and pitfalls these organizations face. We focused on collecting empirical evidence from the participating ERP market players to assess specific facts about the state-of-the-art ERP size and effort estimation practices. Our study adopted a qualitative research method based on an asynchronous online focus group

A systematic review of software development cost estimation studies

This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) Increase the breadth of the search for relevant studies, 2) Search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) Conduct more studies on estimation methods commonly used by the software industry, and, 4) Increase the awareness of how properties of the data sets impact the results when evaluating estimation methods

Complementing Measurements and Real Options Concepts to Support Inter-iteration Decision-Making in Agile Projects

Agile software projects are characterized by iterative and incremental development, accommodation of changes and active customer participation. The process is driven by creating business value for the client, assuming that the client (i) is aware of it, and (ii) is capable to estimate the business value, associated with the separate features of the system to be implemented. This paper is focused on the complementary use of measurement techniques and concepts of real-option-analysis to assist clients in assessing and comparing alternative sets of requirements. Our overall objective is to provide systematic support to clients for the decision-making process on what to implement in each iteration. The design of our approach is justified by using empirical data, published earlier by other authors

From critical success factors to critical success processes

After myriad studies into the main causes of project failure, almost every project manager can list the main factors that distinguish between project failure and project success. These factors are usually called Critical Success Factors (CSF). However, despite the fact that CSF are well-known, the rate of failed projects still remains very high. This may be due to the fact that current CSF are too general and do not contain specific enough know-how to better support project managers decision-making. This paper analyses the impact of 16 specific planning processes on project success and identifies Critical Success Processes (CSP) to which project success is most vulnerable. Results are based on a field study that involved 282 project managers. It was found that the most critical planning processes, which have the greatest impact on project success, are "definition of activities to be performed in the project", "schedule development", "organizational planning", "staff acquisition", "communications planning" and "developing a project plan". It was also found that project managers usually do not divide their time effectively among the different processes, following their influence on project success

Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation

Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world software project databases. We analyze the predictive performance after using the k-NN missing data imputation technique to see if it is better to tolerate missing data or to try to impute missing values and then apply the C4.5 algorithm. For the investigation, we simulated three missingness mechanisms, three missing data patterns, and five missing data percentages. We found that the k-NN imputation can improve the prediction accuracy of C4.5. At the same time, both C4.5 and k-NN are little affected by the missingness mechanism, but that the missing data pattern and the missing data percentage have a strong negative impact upon prediction (or imputation) accuracy particularly if the missing data percentage exceeds 40%