TY - GEN
T1 - Smart Measurements and Analysis for Software Quality Enhancement
AU - Dahab, Sarah
AU - Maag, Stephane
AU - Mallouli, Wissam
AU - Cavalli, Ana
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Requests to improve the quality of software are increasing due to the competition in software industry and the complexity of software development integrating multiple technology domains (e.g., IoT, Big Data, Cloud, Artificial Intelligence, Security Technologies). Measurements collection and analysis is key activity to assess software quality during its development live-cycle. To optimize this activity, our main idea is to periodically select relevant measures to be executed (among a set of possible measures) and automatize their analysis by using a dedicated tool. The proposed solution is integrated in a whole PaaS platform called MEASURE. The tools supporting this activity are Software Metric Suggester tool that recommends metrics of interest according several software development constraints and based on artificial intelligence and MINT tool that correlates collected measurements and provides near real-time recommendations to software development stakeholders (i.e. DevOps team, project manager, human resources manager etc.) to improve the quality of the development process. To illustrate the efficiency of both tools, we created different scenarios on which both approaches are applied. Results show that both tools are complementary and can be used to improve the software development process and thus the final software quality.
AB - Requests to improve the quality of software are increasing due to the competition in software industry and the complexity of software development integrating multiple technology domains (e.g., IoT, Big Data, Cloud, Artificial Intelligence, Security Technologies). Measurements collection and analysis is key activity to assess software quality during its development live-cycle. To optimize this activity, our main idea is to periodically select relevant measures to be executed (among a set of possible measures) and automatize their analysis by using a dedicated tool. The proposed solution is integrated in a whole PaaS platform called MEASURE. The tools supporting this activity are Software Metric Suggester tool that recommends metrics of interest according several software development constraints and based on artificial intelligence and MINT tool that correlates collected measurements and provides near real-time recommendations to software development stakeholders (i.e. DevOps team, project manager, human resources manager etc.) to improve the quality of the development process. To illustrate the efficiency of both tools, we created different scenarios on which both approaches are applied. Results show that both tools are complementary and can be used to improve the software development process and thus the final software quality.
KW - DevOps team
KW - Metrics combination
KW - Metrics correlation
KW - Metrics reuse
KW - Metrics suggestion
KW - Software engineering
KW - Software quality
U2 - 10.1007/978-3-030-29157-0_9
DO - 10.1007/978-3-030-29157-0_9
M3 - Conference contribution
AN - SCOPUS:85071665000
SN - 9783030291563
T3 - Communications in Computer and Information Science
SP - 194
EP - 219
BT - Software Technologies - 13th International Conference, ICSOFT 2018, Revised Selected Papers
A2 - Maciaszek, Leszek A.
A2 - Maciaszek, Leszek A.
A2 - van Sinderen, Marten
PB - Springer Verlag
T2 - 13th International Conference on Software Technologies, ICSOFT 2018
Y2 - 26 July 2018 through 28 July 2018
ER -