This white paper outlines a six-step performance modeling process that combines dynamic performance testing with automated structural quality analysis to enhance the detection of performance and scalability issues during the software development lifecycle. It emphasizes the importance of early identification of latent performance problems through structural analysis of source code, thus preventing costly disruptions in late-stage development. The paper also includes case studies to demonstrate the efficacy of this approach in addressing performance issues.

1. White Paper
6 Steps to Enhance Performance of
Critical Systems
Despite the fact that enterprise IT departments have invested heavily in
dynamic testing tools to verify and validate application performance and
scalability before releasing business applications into production, perfor-
mance issues and response time latency continue to negatively impact
the business. By supplementing dynamic performance testing with
automated structural quality analysis, development teams have the ability
to detect, diagnose, and analyze performance and scalability issues
more effectively. This white paper presents a six-step Performance
Modeling Process using automated structural quality analysis to identify
these potential performance issues earlier in the development lifecycle.

2. Page 2
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6 Steps to Enhance Performance of Critical Systems
I. Introduction
Despite the fact that enterprise IT departments have invested heavily in
dynamic testing tools to verify and validate application performance and
scalability before releasing these business applications into production,
performance issues and response time latency continue to negatively
impact the business.
Application Development and Maintenance (ADM) teams often spot
performance issues in mission-critical applications during the dynamic
or “live” testing phase when an application is almost complete, and
theoretically “ready” for production. By the time they discover these
performance issues, it is too late to make the design or architectural
changes needed to address the issues without business disruption or
costly additional development cycles—resulting in significant delays and/
or business losses.
System-level structural quality analysis provides the ability to detect,
diagnose, and analyze performance and scalability issues. While
performance checks are still seen as the domain of dynamic testing (e.g.
“It does not make sense to solve performance problems by diving into
the code”), automated solutions to analyze and detect performance and
scalability issues based on structural quality analysis of the source code,
are emerging.
By supplementing dynamic performance testing with automated
structural quality analysis, development teams get early and important
information that might be missed with a pure dynamic approach, such
as inefficient loops or SQL queries. The combined approach results in
better detection of latent performance issues within application software.
This white paper presents a six-step Performance Modeling Process
using automated structural quality analysis to identify these potential
performance issues earlier in the development lifecycle. The paper also
presents cases studies to illustrate the proposed modeling process at
work.
II. Approach: Structural Quality Analysis of Source Code to Tackle
Performance Issues
Once upon a time, a seasoned software professional building advanced
military systems used to tell young developers this rather provocative
saying: “You should not optimize, but rather pessimize.”
The developers would laugh at him before finally understanding his
advice. He meant: Do not try to write a sophisticated and difficult
algorithm or query, but rather create a simple and functional one first.
Then optimize those that really need to perform at a very high speed.
Contents
I. Introduction
II. Approach: Structural Quality Analysis
of Source Code to Tackle Performance
Issues
III. Case Studies: Solving Performance Is-
sues with Structural Quality Analysis
IV. The Requirements of a Solid Structural
Quality Analysis Platform
V. Conclusion