Conference proceeding
Mapping architectural decay instances to dependency models
2013 4th International Workshop on Managing Technical Debt (MTD), pp 39-46
May 2013
Abstract
The architectures of software systems tend to drift or erode as they are maintained and evolved. These systems often develop architectural decay instances, which are instances of design decisions that negatively impact a system's lifecycle properties and are the analog to code-level decay instances that are potential targets for refactoring. While code-level decay instances are based on source-level constructs, architectural decay instances are based on higher levels of abstractions, such as components and connectors, and related concepts, such as concerns. Unlike code-level decay instances, architectural decay usually has more significant consequences. Not being able to detect or address architectural decay in time incurs architecture debt that may result in a higher penalty in terms of quality and maintainability (interest) over time. To facilitate architecture debt detection, in this paper, we demonstrate the possibility of transforming architectural models and concerns into an extended augmented constraint network (EACN), which can uniformly model the constraints among design decisions and environmental conditions. From an ACN, a pairwise-dependency relation (PWDR) can be derived, which, in turn, can be used to automatically and uniformly detect architectural decay instances.
Metrics
Details
- Title
- Mapping architectural decay instances to dependency models
- Creators
- Ran Mo - Comput. Sci. Dept., Drexel Univ., Philadelphia, PA, USAJoshua Garcia - Comput. Sci. Dept., Univ. of Southern California, Los Angeles, CA, USAYuanfang Cai - Drexel UniversityNenad Medvidovic - Comput. Sci. Dept., Univ. of Southern California, Los Angeles, CA, USAIEEE
- Publication Details
- 2013 4th International Workshop on Managing Technical Debt (MTD), pp 39-46
- Conference
- 2013 4th International Workshop on Managing Technical Debt (MTD), 4th
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000331830500007
- Scopus ID
- 2-s2.0-84885962628
- Other Identifier
- 991019170150704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Software Engineering