CRSExtractor: Automated configuration option read sites extraction towards IoT cloud infrastructure

Area of Science:

• Computer Science
• Software Engineering
• Cloud Computing

Background:

• Internet of Things (IoT) cloud infrastructures are mature solutions for big data processing.
• Diverse configurations in IoT cloud platforms frequently lead to software configuration errors.
• Manual extraction of configuration read sites from documentation and source code is time-consuming and inefficient.

Purpose of the Study:

• To propose CRSExtractor, an automated technique for extracting configuration read sites.
• To address the limitations of existing manual and automated methods for configuration error troubleshooting.
• To improve the accuracy and efficiency of identifying mappings between configuration options and their source code locations.

Main Methods:

• CRSExtractor employs intra-procedural analysis to identify configuration read sites.
• The technique automatically maps configuration options to their corresponding locations in the source code.
• Evaluations were conducted on core IoT cloud platform software systems like Hadoop and Cassandra.

Main Results:

• CRSExtractor achieved an accuracy rate exceeding 90% in identifying configuration read sites.
• The proposed method demonstrated efficiency nearly 20 times faster than previous approaches.
• Automated mapping of configuration options to source code read sites was successfully implemented.

Conclusions:

• CRSExtractor offers an effective automated solution for identifying configuration read sites in IoT cloud environments.
• The technique significantly improves upon existing methods in terms of accuracy and speed.
• This advancement facilitates more efficient troubleshooting of software configuration errors in complex IoT systems.