Conference proceeding
Measuring Architecture Quality by Structure Plus History Analysis
PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), pp 891-900
01 Jan 2013
Abstract
This case study combines known software structure and revision history analysis techniques, in known and new ways, to predict bug-related change frequency, and uncover architecture-related risks in an agile industrial software development project. We applied a suite of structure and history measures and statistically analyzed the correlations between them. We detected architecture issues by identifying outliers in the distributions of measured values and investigating the architectural significance of the associated classes. We used a clustering method to identify sets of files that often change together without being structurally close together, investigating whether architecture issues were among the root causes. The development team confirmed that the identified clusters reflected significant architectural violations, unstable key interfaces, and important undocumented assumptions shared between modules. The combined structure diagrams and history data justified a refactoring proposal that was accepted by the project manager and implemented.
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Details
- Title
- Measuring Architecture Quality by Structure Plus History Analysis
- Creators
- Robert Schwanke - Siemens Corp, Corp Technol, Princeton, NJ 08540 USALu Xiao - Drexel Univ, Dept Comp Sci, Philadelphia, PA 19104 USAYuanfang Cai - Drexel University
- Contributors
- D Notkin (Editor)BHC Cheng (Editor)K Pohl (Editor)
- Publication Details
- PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), pp 891-900
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- CCF-0916891; CCF-1065189; CCF-1116980 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000333965800092
- Scopus ID
- 2-s2.0-84886412212
- Other Identifier
- 991019170470004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Software Engineering
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic