The WyCash portfolio management system
OOPSLA '92 Addendum to the proceedings on Object-oriented programming systems, languages, and applications (Addendum)
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices
Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices
ACM SIGPLAN Notices
Source Code Analysis: A Road Map
FOSE '07 2007 Future of Software Engineering
Prioritizing Warning Categories by Analyzing Software History
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Which warnings should I fix first?
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Empirical studies in reverse engineering: state of the art and future trends
Empirical Software Engineering
How Software Designs Decay: A Pilot Study of Pattern Evolution
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Guide to Advanced Empirical Software Engineering
Guide to Advanced Empirical Software Engineering
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Proceedings of the 19th international symposium on Software testing and analysis
Managing technical debt in software-reliant systems
Proceedings of the FSE/SDP workshop on Future of software engineering research
Detecting software modularity violations
Proceedings of the 33rd International Conference on Software Engineering
| Hi-index | 0.00 |
The technical debt (TD) metaphor describes a tradeoff between short-term and long-term goals in software development. Developers, in such situations, accept compromises in one dimension (e.g. maintainability) to meet an urgent demand in another dimension (e.g. delivering a release on time). Since TD produces interests in terms of time spent to correct the code and accomplish quality goals, accumulation of TD in software systems is dangerous because it could lead to more difficult and expensive maintenance. The research presented in this paper is focused on the usage of automatic static analysis to identify Technical Debt at code level with respect to different quality dimensions. The methodological approach is that of Empirical Software Engineering and both past and current achieved results are presented, focusing on functionality, efficiency and maintainability.