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https://dx.doi.org/10.7892/bor...
Doctoral thesis . 2025
Data sources: Datacite
https://dx.doi.org/10.24442/bo...
Other literature type . 2017
Data sources: Datacite
DBLP
Doctoral thesis . 2023
Data sources: DBLP
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Quality-Aware Tooling

Authors: Tymchuk, Yuriy;

Quality-Aware Tooling

Abstract

Programming is a fascinating activity that can yield results capable of changing people lives by automating daily tasks or even completely reimagining how we perform certain activities. Such a great power comes with a handful of challenges, with software maintainability being one of them. Maintainability cannot be validated by executing the program but has to be assessed by analyzing the codebase. This tedious task can be also automated by the means of software development. Programs called static analyzers can process source code and try to detect suspicious patterns. While these programs were proven to be useful, there is also an evidence that they are not used in practice. In this dissertation we discuss the concept of quality-aware tooling —- an approach that seeks a promotion of static analysis by seamlessly integrating it into development tools. We describe our experience of applying quality-aware tooling on a core distribution of a development environment. Our main focus is to provide live quality feedback in the code editor, but we also integrate static analysis into other tools based on our code quality model. We analyzed the attitude of the developers towards the integrated static analysis and assessed the impact of the integration on the development ecosystem. As a result 90% of software developers find the live feedback useful, quality rules received an overhaul to better match the contemporary development practices, and some developers even experimented with a custom analysis implementations. We discovered that live feedback helped developers to avoid dangerous mistakes, saved time, and taught valuable concepts. But most importantly we changed the developers' attitude towards static analysis from viewing it as just another tool to seeing it as an integral part of their toolset.

Country
Switzerland
Related Organizations
Keywords

knowledge & systems, 510 Mathematics, 000 Computer science, 000 Computer science, knowledge & systems, 000 Computer science, knowledge & systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green