IWSED-95: International Workshop on Software Engineering Data

Assessment of product and process quality

Reported by Reidar Conradi and Jyrki Kontio (Note: this section is incomplete, this report was prepared from presentation notes)

Participants:

Yasuhiro Mashiko, Matsushita

Richard Stenglain, Motorola, co-chair

• Giovanni Cantone
• Steel Huang, AT&T
• Tony Jordano, SAIC
• Sandro Morasca
• Santiago Rementeria, European Software Institute
• Keishi Sakamoto, OMRON
• Franz Schweiggert, University of Ulm
• Shingo Takada, Nara Institute
• Zhijun (William) Zhang, University of Maryland

Working Group Goal and issues addressed

• Empirical data on the relationship between process and product quality: what exactly is the relationship between the two ?
• How to deal with multiple goals for quality and between their trade-offs
• How to integrate external reference models (e.g., ISO 9001 or CMM) with improvement models based on organizational, in-house learning

Discussion

many organizations start their improvement projects in a state where initial improvement are easy to make and most changes result in improvements.

In general, there is low awareness for product and process quality improvement. However, AT&T's 5ESS project, Motorola, SAIC, OMRON etc., have big initiatives.

Technology jumps to new paradigms:

• First reduce defects,
• Then reduce #processes, apply reuse,
• Then ...

Different SPI "phases": certain technologies are appropriate in each.

General support: Project management

Quality management

Configuration management

How to define quality?

• Defects, effort, schedule, customer satisfaction,
• Maintainability, ...

Problems and issues:

• Multiple goals, trade-offs
• SPI loop, fed by business needs and goals
• SPI not goal, rather profitability.
• How to predict costs?
• Resistance to change
• Software goals vs. business goals.

State-of-practice

• Defect-driven.
• ISO-9000 is waterfall-driven.

Existing solutions

• Inspections, but needing clear exit criteria on defect removal, data capture, learning.
• From assessment to improvement plan.

Research

• Product assessment
• Software dynamics
• Failure-mode effect analysis

Conclusions

<TBD >

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Updated 06-Mar-96 by Jyrki Kontio

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