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teaching:reverse:2018:references [2018/12/13 10:42] blay [Etudes] |
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| * doi = {10.1145/3194164.3194176} | * doi = {10.1145/3194164.3194176} | ||
| * Architectural Technical Debt (ATD) regards sub-optimal design decisions that bring short-term benefits to the cost of long-term gradual deterioration of the quality of the architecture of a software system. The identification of ATD strongly influences the technical and economic sustainability of software systems and is attracting growing interest in the scientific community. During the years several approaches for ATD identification have been conceived, each of them addressing ATD from different perspectives and with heterogeneous characteristics. In this paper we apply the systematic mapping study methodology for identifying, classifying, and evaluating the state of the art on ATD identification from the following three perspectives: publication trends, characteristics, and potential for industrial adoption. Specifically, starting from a set of 509 potentially relevant studies, we systematically selected 47 primary studies and analyzed them according to a rigorously-defined classification framework. The analysis of the obtained results supports both researchers and practitioners by providing (i) an assessment of current research trends and gaps in ATD identification, (ii) a solid foundation for understanding existing (and future) research on ATD identification, and (iii) a rigorous evaluation of its potential for industrial adoption. | * Architectural Technical Debt (ATD) regards sub-optimal design decisions that bring short-term benefits to the cost of long-term gradual deterioration of the quality of the architecture of a software system. The identification of ATD strongly influences the technical and economic sustainability of software systems and is attracting growing interest in the scientific community. During the years several approaches for ATD identification have been conceived, each of them addressing ATD from different perspectives and with heterogeneous characteristics. In this paper we apply the systematic mapping study methodology for identifying, classifying, and evaluating the state of the art on ATD identification from the following three perspectives: publication trends, characteristics, and potential for industrial adoption. Specifically, starting from a set of 509 potentially relevant studies, we systematically selected 47 primary studies and analyzed them according to a rigorously-defined classification framework. The analysis of the obtained results supports both researchers and practitioners by providing (i) an assessment of current research trends and gaps in ATD identification, (ii) a solid foundation for understanding existing (and future) research on ATD identification, and (iii) a rigorous evaluation of its potential for industrial adoption. | ||
| - | + | - [[ https://www.cs.drexel.edu/~lx52/LuXiao/papers/FSE-TD-14.pdf|Titan: A Toolset That Connects Software Architecture with Quality Analysis]] | |
| + | * Lu Xiao, Yuanfang Cai, and Rick Kazman. | ||
| + | * 2014. | ||
| + | * ACM SIGSOFT International Symposium on Foundations of Software Engineering. | ||
| + | * 763–766 | ||
| + | - [[http://aircconline.com/ijsea/V9N5/9518ijsea01.pdf|[INVESTIGATE,IDENTIFY AND ESTIMATE THE TECHNICAL DEBT: A SYSTEMATIC MAPPING STUDY]] | ||
| + | * Benidris, M., Ammar, H., & Dzielski, D. | ||
| + | * 2018 | ||
| + | * International Journal of Software Engineering & Applications (IJSEA). | ||
| + | * https://doi.org/10.5121/ijsea.2018.9501 | ||
| </panel> | </panel> | ||
| </accordion> | </accordion> | ||
teaching/reverse/2018/references.txt · Last modified: 2018/12/16 17:40 by blay
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