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A System and Method for Cognitive Code Quality Measurement

IP.com Disclosure Number: IPCOM000244584D
Publication Date: 2015-Dec-23
Document File: 4 page(s) / 109K

Publishing Venue

The IP.com Prior Art Database

Abstract

In this article, we propose a framework of cognitive code quality measurement. The core idea is to leverage the relations between emotions and behaviors via cognition to identify the potential failure-causing codes. Mobile devices will be used to monitor developer emotion data, and then upload to backend emtion server for analyzing whether certain failure-causing emotion pattern is detected or not. Considering the bio features varies for individuals, we combine general pattern recognition & personal features on emotion/failure-causing code. As such, we can find the failure-causing codes related to improper emotions as early as possible for individual developers, and control the code scale of suspicious failure-causing codes.

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A System and Method for Cognitive Code Quality Measurement

Different biological status may cause the developer to behave differently when coding. For example, when a developer feels tired, he may not be able to think carefully how his developed code may affect other related codes. Therefore, it is important to monitor the bio status of the developer. Nowadays, as mobile devices are increasingly popular for daily usage purposes, it is possible to record the developer bio status.

Here is an intuitive example showing the cases when a developer's emotion is abnormal (e.g., anxious, sleepy), he/she may not be able to examine code carefully, which will lead to certain fault. For example, the variable i maybe mistakenly written into j.

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To find the failure-causing codes related to improper emotions as early as possible, we propose the following framework of cognitive code quality measurement.

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Here is the description of major steps:

1) When a developer is coding, the plug-in embedded in the Development IDE periodically calculates the code updates and commits to the code repository.

2) Backend code sever adds temporal properties for the code updates, and conducts static quantification of code risks.

3) Static quantification of code risks is submitted for the risk consolidation with emotion risks.

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4) Developer emotion data is captured via mobile or werable devices.

5) Mobile devices upload developer emotion data and mobility dat...