Architectural Degradation and Technical Debt Dashboards

Dario Amoroso d’Aragona

doi:10.1007/978-3-031-21388-5_49

Architectural Degradation and Technical Debt Dashboards

Dario Amoroso d’Aragona^*

^*Corresponding author for this work

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

19 Downloads (Pure)

Abstract

Background. Companies frequently try to improve the quality of their software by resolving technical issues that are perceived to have an impact on software quality. Technical information is any information that may be gathered from the source code or the software development process, for instance: code or documentation guidelines, and the use of specific patterns. If these issues are not fixed they may generate technical debt. Goal. The goal of the Ph.D., which started on January 2022, is to understand which are the causes of Architectural Technical Debt in a real case study and to develop a Dashboard to notify developers as earlier as possible. Methods and expected contribution. We first plan to investigate the actual production code process and then, and then to work with teams to find the best practices and strategies to easily manage Architectural Technical Debt.

Original language	English
Title of host publication	Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings
Editors	Davide Taibi, Marco Kuhrmann, Tommi Mikkonen, Pekka Abrahamsson, Jil Klünder
Publisher	Springer
Pages	638-643
Number of pages	6
ISBN (Print)	9783031213878
DOIs	https://doi.org/10.1007/978-3-031-21388-5_49
Publication status	Published - 2022
Publication type	A4 Article in conference proceedings
Event	International Conference on Product-Focused Software Process Improvement - Jyväskylä, Finland Duration: 21 Nov 2022 → 23 Nov 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13709 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Product-Focused Software Process Improvement
Country/Territory	Finland
City	Jyväskylä
Period	21/11/22 → 23/11/22

Funding

This work is funded by the Doctoral School of Industry Innovation (DSII), and ABB.

Keywords

Architectural debt
Software quality
Technical debt

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-21388-5_49

Architectural Degradation Dashboard
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Final published version, 158 KB

https://urn.fi/URN:NBN:fi:tuni-202302212555

Cite this

Amoroso d’Aragona, D. (2022). Architectural Degradation and Technical Debt Dashboards. In D. Taibi, M. Kuhrmann, T. Mikkonen, P. Abrahamsson, & J. Klünder (Eds.), Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings (pp. 638-643). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13709 LNCS). Springer. https://doi.org/10.1007/978-3-031-21388-5_49

Amoroso d’Aragona, Dario. / Architectural Degradation and Technical Debt Dashboards. Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings. editor / Davide Taibi ; Marco Kuhrmann ; Tommi Mikkonen ; Pekka Abrahamsson ; Jil Klünder. Springer, 2022. pp. 638-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5c64a524ef5948369013db796820cc78,

title = "Architectural Degradation and Technical Debt Dashboards",

abstract = "Background. Companies frequently try to improve the quality of their software by resolving technical issues that are perceived to have an impact on software quality. Technical information is any information that may be gathered from the source code or the software development process, for instance: code or documentation guidelines, and the use of specific patterns. If these issues are not fixed they may generate technical debt. Goal. The goal of the Ph.D., which started on January 2022, is to understand which are the causes of Architectural Technical Debt in a real case study and to develop a Dashboard to notify developers as earlier as possible. Methods and expected contribution. We first plan to investigate the actual production code process and then, and then to work with teams to find the best practices and strategies to easily manage Architectural Technical Debt.",

keywords = "Architectural debt, Software quality, Technical debt",

author = "\{Amoroso d{\textquoteright}Aragona\}, Dario",

note = "Funding Information: This work is funded by the Doctoral School of Industry Innovation (DSII), and ABB. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Product-Focused Software Process Improvement ; Conference date: 21-11-2022 Through 23-11-2022",

year = "2022",

doi = "10.1007/978-3-031-21388-5\_49",

language = "English",

isbn = "9783031213878",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "638--643",

editor = "Davide Taibi and Marco Kuhrmann and Tommi Mikkonen and Pekka Abrahamsson and Jil Kl{\"u}nder",

booktitle = "Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings",

}

Amoroso d’Aragona, D 2022, Architectural Degradation and Technical Debt Dashboards. in D Taibi, M Kuhrmann, T Mikkonen, P Abrahamsson & J Klünder (eds), Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13709 LNCS, Springer, pp. 638-643, International Conference on Product-Focused Software Process Improvement, Jyväskylä, Finland, 21/11/22. https://doi.org/10.1007/978-3-031-21388-5_49

Architectural Degradation and Technical Debt Dashboards. / Amoroso d’Aragona, Dario.
Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings. ed. / Davide Taibi; Marco Kuhrmann; Tommi Mikkonen; Pekka Abrahamsson; Jil Klünder. Springer, 2022. p. 638-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13709 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Architectural Degradation and Technical Debt Dashboards

AU - Amoroso d’Aragona, Dario

N1 - Funding Information: This work is funded by the Doctoral School of Industry Innovation (DSII), and ABB. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Background. Companies frequently try to improve the quality of their software by resolving technical issues that are perceived to have an impact on software quality. Technical information is any information that may be gathered from the source code or the software development process, for instance: code or documentation guidelines, and the use of specific patterns. If these issues are not fixed they may generate technical debt. Goal. The goal of the Ph.D., which started on January 2022, is to understand which are the causes of Architectural Technical Debt in a real case study and to develop a Dashboard to notify developers as earlier as possible. Methods and expected contribution. We first plan to investigate the actual production code process and then, and then to work with teams to find the best practices and strategies to easily manage Architectural Technical Debt.

AB - Background. Companies frequently try to improve the quality of their software by resolving technical issues that are perceived to have an impact on software quality. Technical information is any information that may be gathered from the source code or the software development process, for instance: code or documentation guidelines, and the use of specific patterns. If these issues are not fixed they may generate technical debt. Goal. The goal of the Ph.D., which started on January 2022, is to understand which are the causes of Architectural Technical Debt in a real case study and to develop a Dashboard to notify developers as earlier as possible. Methods and expected contribution. We first plan to investigate the actual production code process and then, and then to work with teams to find the best practices and strategies to easily manage Architectural Technical Debt.

KW - Architectural debt

KW - Software quality

KW - Technical debt

U2 - 10.1007/978-3-031-21388-5_49

DO - 10.1007/978-3-031-21388-5_49

M3 - Conference contribution

AN - SCOPUS:85142707107

SN - 9783031213878

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 638

EP - 643

BT - Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings

A2 - Taibi, Davide

A2 - Kuhrmann, Marco

A2 - Mikkonen, Tommi

A2 - Abrahamsson, Pekka

A2 - Klünder, Jil

PB - Springer

T2 - International Conference on Product-Focused Software Process Improvement

Y2 - 21 November 2022 through 23 November 2022

ER -

Amoroso d’Aragona D. Architectural Degradation and Technical Debt Dashboards. In Taibi D, Kuhrmann M, Mikkonen T, Abrahamsson P, Klünder J, editors, Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings. Springer. 2022. p. 638-643. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-21388-5_49