4. APPLICATIONS/BUSINESS LAYER: This layer outlines the wide range of available applications/products that can readily make use of the processed data sets and leverage by catering most promising and optimising services across different business domains or segments.
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F U N C T I O N A L
T E S T I N G
IS IOT DATA SETS CENTRIC OR DATA-SETS DRIVEN?
As of today, it’s still an argumentative debate to understand and appreciate the fact IoT is data-set centric or data-set driven. But, taking the aforementioned IoT ecosystem explanation into consideration, it is very evident that there is a co-existence of number of heterogeneous entities which are logically tied together and completely driven and processed data-sets flowing from one layer to controlled by rich data-sets i.e. without the processed data-sets Blowing from one layer another in the ecosystem, it is definitely hard to another in the ecosystem, it is deBinitely hard to imagine the existence of the IoT.
to imagine the existence of the IoT.
It is learnt that IoT is data (or data-sets) driven, being it an interactional or transactional in nature. Now, surprisingly, the very driving factor itself has turned out to be a biggest challenge for IoT because per Gartner, by 2020, there will be over 26 billion connected devices, and on similar lines one other research (Morgan Stanley, Hammersmith Group) study forecasts that the count could go up to 100 billion, which scientiBically or statistically means there will be at least three times more IoT devices in comparison to world population. This straight away technically infers that the underlying data that is ingested, transformed and exchanged is going to be increased in unprecedented volume, which gives rise to following two fundamental challenges. •
Sr. No
Processing Layer
Data-sets Blowing across layers
Data Ingestion / Transform Layer
Devices Layer
IOT KEY CHALLENGES It is learnt that IoT is data (or data-sets) driven, being it an interactional or transactional in nature. Now, surprisingly, the very driving factor itself has turned out to be a biggest challenge for IoT because per Gartner, by 2020, there will be over 26 billion connected devices, and on similar lines one other research (Morgan Stanley, Hammersmith Group) study forecasts that the count could go up to 100 billion, which scientifically or statistically means there will be at least three times more IoT devices in comparison to world population. This straight away technically infers that the underlying data that is ingested, transformed and exchanged is going to be increased in unprecedented volume, which gives rise to following two fundamental challenges. • Storing, administering and handling such huge volume of data-sets. • Ascertaining the quality characteristics (both functional & non-functional) to ensure that IoT devices perform as intended both at a component level as well as a data-set level.
Test Scope
Sensors
▪ ▪ ▪ ▪ ▪
2
Interfacing Application
▪ ▪ ▪ ▪
3
Network Communication
Underlying / Supporting Database
▪ ▪ ▪ ▪ ▪
Device Hardware Embedded Software Performance Device Response Time Fault Control / Handling Mechanism Functionality UI Capabilities Role and Access controls Request handling Connectivity Data transmission
Data consistency Data transmission/loss Data encryption/decryption
Importantly, it is learnt that a combination of functional + non-functional testing is more effective and result oriented when compared to implementing test automation, for the following major reasons: • Primarily, IoT is not for a specific business domain, and likewise, the corresponding field of application is never the same. • Diverse business domains are driven by bundles of complex functionalities, which demands expert intervention and validation. • Above all, it is imperative to have embedded testing both considered and included on top of regular testing for IoT, which is not a correct grouping for test automation.
TESTING TYPES MATRIX FOR IOT Importantly, it is learnt that a combination of functional + non-functional testing is more effective and result oriented when compared to implementing test automation, for the
following major reasons: Now since it is learnt that IoT is data-set • Primarily, IoT is not for a speciBic business domain, and likewise, the corresponding driven, with data being assimilated from Bield of application is never the same. different sources indriven an by IoT platform, it is which • Diverse business domains are bundles of complex functionalities, demands expert intervention and validation. critical to have testing conducted across • Above all, it is imperative to have embedded testing both considered and included on following testing types. The below grid top of regular testing for IoT, which is not a correct grouping for test automation. TESTING TYPES MATRIX FOR IOT outlines the holistic view of the different, Now since it is learnt that IoT is data-set driven, with data being assimilated from major (and recommended) testing types for different sources in an IoT platform, it is critical to have testing conducted across following testing types. The below grid outlines the holistic view of the different, major validating IoT platform: (and recommended) testing types for validating IoT platform: Sr. No
Objectives
Functional Testing
Validate the functionality of the application against requirements
3
Usability Testing
Primarily to assess user friendliness of the application
Database Testing
4
Performance Testing
5
Security Testing
7
Compatibility Testing
6
Testing Type
1 2
IOT From a testing standpoint, knowing the IoT ecosystem alone will not help test specialists formulate a comprehensive test strategy or define a test approach, but it is equally critical to know and attain detailed understanding about the essential key components that
IoT Component
1
4
TESTING APPROACH FOR
T E S T M a g a z i n e | M a y 2 01 8
Storing, administering and handling such huge volume of data-sets.
• Ascertaining the quality characteristics (both functional & non-functional) to ensure function together and collaborate to have an that IoT devices perform as intended both at a component level as well as a data-set IoTlevel. work as intended. So, once we have the TESTING APPROACH FOR IOT list of components, one can easily ascertain From a testing standpoint, knowing the IoT ecosystem alone will not help test specialists the test scope. The below grid outlines formulate a comprehensive test strategy or deBine a test approach, but it is equally critical to know and attain detailed understanding about the essential key components the details of the key IoT components and that function together and collaborate to have an IoT work as intended. So, once we have corresponding test scope details: the list of components, one can easily ascertain the test scope. The below grid outlines the details of the key IoT components and corresponding test scope details:
Applications / Business Layer
IOT KEY CHALLENGES
Network Testing
Validate data types, values, integrity and data consistency
Validate the response time of the components (sensors etc.) Validate the data reading, writing and retrieval ability of component. Validating data privacy, network security, user roles and corresponding access.
Validate connectivity and underlying protocols.
Validate functioning of components across varied device hardware, communication protocols, operating systems etc.