Mon 5 Nov 2018 11:15 - 11:37 at Rock Lake - Session 2: Defect prediction

Our industrial experience in institutionalizing defect prediction models in the software industry shows that the first step is to measure prediction metrics and defects to assess the feasibility of the tool, i.e., if the accuracy of the defect prediction tool is higher than of a random predictor. However, computing prediction metrics is time consuming and error prone. Thus, the feasibility analysis has a cost which needs some initial investment by the potential clients. This initial investment acts as a barrier for convincing potential clients of the benefits of institutionalizing a software prediction model. To reduce this barrier, in this paper we present the Pilot Defects Prediction Dataset Maker (PDPDM), a desktop application for measuring metrics to use for defect prediction. PDPDM receives as input the repository’s information of a software project, and it provides as output, in an easy and replicable way, a dataset containing a set of 17 well-defined product and process metrics, that have been shown to be useful for defect prediction, such as size and smells. PDPDM avoids the use of outdated datasets and it allows researchers and practitioners to create defect datasets without the need to write any lines of code.

Mon 5 Nov
Times are displayed in time zone: (GMT-05:00) Guadalajara, Mexico City, Monterrey change

11:15 - 12:00: SWAN - Session 2: Defect prediction at Rock Lake
fse-2018-SWAN11:15 - 11:37
Davide FalessiCalifornia Polytechnic State University, Max Jason MoedeCalifornia Polytechnic State University, USA
fse-2018-SWAN11:37 - 12:00
Huy TuNorth Carolina State University, USA, Vivek Nair