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code_call_lens: raising the developer awareness of critical code

Published: 03 September 2018 Publication History

Abstract

As a developer, it is often complex to foresee the impact of changes in source code on usage, e.g., it is time-consuming to find out all components that will be impacted by a change or estimate the impact on the usability of a failing piece of code. It is therefore hard to decide how much effort in quality assurance is justifiable to obtain the desired business goals. In this paper, to reduce the difficulty for developers to understand the importance of source code, we propose an automated way to provide this information to developers as they are working on a given piece of code. As a proof-of-concept, we developed a plug-in for Microsoft Visual Studio Code that informs about the importance of source code methods based on the frequency of usage by the end-users of the developed software. The plug-in aims to increase the awareness developers have about the importance of source code in an unobtrusive way, helping them to prioritize their effort to quality assurance, technical excellence, and usability. code_call_lens can be downloaded from GitHub at https://github.com/xxMUROxx/vscode.code_call_lens.

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P.Johnson. 2001. Can’t even ask them to push a button: Toward ubiquitous, developer-centric, empirical software engineering. In Visions for Software Design and Productivity: Research and Applications.
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Cited By

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  • (2024)Impermanent identifiers: Enhanced source code comprehension and refactoringJournal of Systems and Software10.1016/j.jss.2024.112137216(112137)Online publication date: Oct-2024
  • (2024)Studying the impact of risk assessment analytics on risk awareness and code review performanceEmpirical Software Engineering10.1007/s10664-024-10443-x29:2Online publication date: 17-Feb-2024
  • (2020)Visualization of Methods Changeability Based on VCS DataProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387451(477-480)Online publication date: 29-Jun-2020

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    cover image ACM Conferences
    ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
    September 2018
    955 pages
    ISBN:9781450359375
    DOI:10.1145/3238147
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 03 September 2018

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    Author Tags

    1. Empirical Software Engineering
    2. User tracking

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    View all
    • (2024)Impermanent identifiers: Enhanced source code comprehension and refactoringJournal of Systems and Software10.1016/j.jss.2024.112137216(112137)Online publication date: Oct-2024
    • (2024)Studying the impact of risk assessment analytics on risk awareness and code review performanceEmpirical Software Engineering10.1007/s10664-024-10443-x29:2Online publication date: 17-Feb-2024
    • (2020)Visualization of Methods Changeability Based on VCS DataProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387451(477-480)Online publication date: 29-Jun-2020

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