SPDW+: a seamless approach for capturing quality metrics in software development environments

Abstract

Among the key factors for the success of a metrics program are the regularity of metrics collection, a seamless and efficient data collection methodology, and the presence of non-intrusive automated data collection tools. This paper presents the software process data warehousing architecture SPDW? as a solution to the frequent, seamless, and automated capturing of software quality metrics, and their integration in a central repository for a full range of analyses. The striking features of the SPDW? ETL (data extraction, transformation, and loading) approach are that it addresses heterogeneity issues related to the software development context, it is automatable and non-intrusive, and it allows different capturing frequency and latency strategies, hence allowing both analysis and monitoring of software metrics. The paper also provides a reference framework that details three orthogonal dimensions for considering ETL issues in the software development process context, used to develop SPDW? ETL. The advantages of SPDW? are: (1) flexibility to meet the requirements of the frequent changes in SDP environments; (2) support for monitoring, which implies the execution of frequent and incremental loads; (3) automation of the complex and time-consuming task of capturing metrics, making it seamless; (4) freedom of choice regarding management models and support tools used in projects; and (5) cohesion and consistency of the information stored in the metrics repository which will be used to compare data of different projects. The paper presents the reference framework, illustrates the key role played by the metrics capturing process in a metrics program using a case study, and presents the striking features of SPDW? and its ETL approach, as well as an evaluation based on a prototype implementation.

A Levels-based Approach for Defining Software Measurement Architectures

“…Most of the proposals include mechanisms for data analysis and presentation. Some proposals, such as SPDW+ [26] and PAU [28], have more complex mechanisms and tools. The analysis can be purely qualitative, as in WebEv [22,23], or have a benchmark type, as in NSDIR [24] and MRS [25], in which general data of products and projects can be analyzed to support identification of best practices.…”

“…Finally, we observed that the proposals that include support to statistical process control (SPDW+ [26] e MMR Tool [17]) adopt solutions based on data warehouse.…”

“…The proposals MRS [25] and NSDIR [24] have specific client programs for communication with the server. WebEv [22,23], MMR Tool [17], and SPDW+ [26], in turn, use web resources. The proposals SPDW+ [26] e MMR Tool [17] have architectures based on data warehouse environment, including a component for data collection (ETL), a component to storage (data warehouse), and a component for analysis with analytical capabilities (OLAP).…”

“…WebEv [22,23], MMR Tool [17], and SPDW+ [26], in turn, use web resources. The proposals SPDW+ [26] e MMR Tool [17] have architectures based on data warehouse environment, including a component for data collection (ETL), a component to storage (data warehouse), and a component for analysis with analytical capabilities (OLAP). The SPDW+ [26] includes a fourth component responsible for the data integration.…”

“…The publications which describe the proposals MMR Tool [17] e MRS [25] mention the intention of using automatic collection mechanisms, but these mechanisms are not described. Only two proposals implemented the automatic collection: Generic Measurement Based on MDA, [16,18,19,20], by means of models transformation; and SPDW+ [26], with a ETL component. It is important to emphasize that these proposals deal with very specific types of measures (for instance, quantity of tables and relationships in a certain data model, and number of errors in a portion of source code), which are more favorable for automatic collection.…”