In this paper, we propose a method for predicting development effort based on user stories. Such approach is well suited for Agile software projects where requirements are developed along with the project and only sketched in a rough manner. We apply the proposed method to two industrial Agile software projects of very different size and structure. We show that such effort estimation works reasonably well if user stories are written in a structured way.

In this paper, we describe an experiment, which analyzes the relative importance and stability of change metrics for predicting defects for 3 releases of the Eclipse project. The results indicate that out of 18 change metrics 3 metrics contain most information about software defects. Moreover, those 3 metrics remain stable across 3 releases of the Eclipse project. A comparative analysis with the full model shows that the prediction accuracy is not too much affected by using a subset of 3 metrics and the recall even improves.

In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.

Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.