Mike Konrad

Capability Maturity ModelingSM at the SEI

In 1987, the SEI released a software process maturity framework and maturity questionnaire to support organizations in improving their software process. Four years later, the SEI released the Capability Maturity ModelSM for Software (SW-CMMSM). The SW-CMM has influenced software process improvement worldwide to a significant degree. More recently, the SEI has become involved in developing additional capability maturity models that impact software. This paper discusses the problems these CMMs are trying to address, our goals in developing these CMMs, the objectives and status of each of these models, and our current plans for the 1996–1997 time frame. We then briefly turn to topics that address the usability of the SW-CMM in certain situations: in small organizations and in challenging application domains. We then describe SEIs involvement in an international standards effort to create a standard for software process assessment. Finally, to gain perspective on how the CMMs might impact the community in the future, we look at the growing use of the SW-CMM and some benefits associated with its use.

Attention to process and people are key to technology adoption

It is the position of the author that attention to both process and people is a key to rapidly adopting new technology essential to improving software quality, reducing costs, and shortening cycle time. In order to succeed, corporations are realizing the importance of meeting these challenges. The author considers how many organizations are trying to address these challenges through the introduction of new technology, such as object oriented design and CASE.

Preliminary Causal Discovery Results with Software Effort Estimation Data

Correlation does not imply causation. Though this is a well-known fact, most analyses depend on correlation as proof of relationships that are often treated as causal. Causal discovery, also referred to as causal model search, involves the application of statistical methods to identify causal relationships from conditional independences (and/or other statistical relationships) in the data. Though software cost estimation models use both domain knowledge and statistics, to date, there has yet to be a published report describing the evaluation of a software dataset using causal discovery. Two of the authors have previously used regression analysis to evaluate the effectiveness of the International Function Points User Group (IFPUG)s and the Common Software Measurement International Consortium (COSMIC)s functional size measurement methods for analyzing the Unified Code Count (UCC)1s dataset of maintenance tasks. Using the same dataset, the authors will report in this paper on what types of information causal discovery provides, and how they differ from correlation tests. This paper will introduce causal discovery to software engineering research, and its use in the future may impact how software effort models are built.