rapids/docs/developers/test-cases.md

Test Cases

Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the tests/data/raw/test01/ contains all the test data files for testing android data formats and tests/data/raw/test02/ contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the tests/data/processed/test01/ and tests/data/processed/test02/ for Android and iOS respectively. tests/data/raw/test03/ and tests/data/raw/test04/ contain data files for testing empty raw data files for android and iOS respectively.

The following is a list of the sensors that testing is currently available.

Sensor	Provider	Periodic	Frequency	Event
Phone Accelerometer	Panda	N	N	N
Phone Accelerometer	RAPIDS	N	N	N
Phone Activity Recognition	RAPIDS	N	N	N
Phone Applications Foreground	RAPIDS	N	N	N
Phone Battery	RAPIDS	Y	Y	N
Phone Bluetooth	Doryab	N	N	N
Phone Bluetooth	RAPIDS	Y	Y	Y
Phone Calls	RAPIDS	Y	Y	N
Phone Conversation	RAPIDS	Y	Y	N
Phone Data Yield	RAPIDS	N	N	N
Phone Light	RAPIDS	Y	Y	N
Phone Locations	Doryab	N	N	N
Phone Locations	Barnett	N	N	N
Phone Messages	RAPIDS	Y	Y	N
Phone Screen	RAPIDS	Y	N	N
Phone WiFi Connected	RAPIDS	Y	Y	N
Phone WiFi Visible	RAPIDS	Y	Y	N
Fitbit Calories Intraday	RAPIDS	Y	Y	Y
Fitbit Data Yield	RAPIDS	N	N	N
Fitbit Heart Rate Summary	RAPIDS	N	N	N
Fitbit Heart Rate Intraday	RAPIDS	N	N	N
Fitbit Sleep Summary	RAPIDS	N	N	N
Fitbit Sleep Intraday	RAPIDS	Y	Y	Y
Fitbit Sleep Intraday	PRICE	Y	Y	Y
Fitbit Steps Summary	RAPIDS	N	N	N
Fitbit Steps Intraday	RAPIDS	N	N	N

Messages (SMS)

• The raw message data file contains data for 2 separate days.
• The data for the first day contains records 5 records for every epoch.
• The second day's data contains 6 records for each of only 2 epoch (currently morning and evening)
• The raw message data contains records for both message_types (i.e. recieved and sent) in both days in all epochs. The number records with each message_types per epoch is randomly distributed There is at least one records with each message_types per epoch.
• There is one raw message data file each, as described above, for testing both iOS and Android data.
• There is also an additional empty data file for both android and iOS for testing empty data files

Calls

Due to the difference in the format of the raw call data for iOS and Android the following is the expected results the calls_with_datetime_unified.csv. This would give a better idea of the use cases being tested since the calls_with_datetime_unified.csv would make both the iOS and Android data comparable.

• The call data would contain data for 2 days.
• The data for the first day contains 6 records for every epoch.
• The second day's data contains 6 records for each of only 2 epoch (currently morning and evening)
• The call data contains records for all call_types (i.e. incoming, outgoing and missed) in both days in all epochs. The number records with each of the call_types per epoch is randomly distributed. There is at least one records with each call_types per epoch.
• There is one call data file each, as described above, for testing both iOS and Android data.
• There is also an additional empty data file for both android and iOS for testing empty data files

Screen

Due to the difference in the format of the raw screen data for iOS and Android the following is the expected results the screen_deltas.csv. This would give a better idea of the use cases being tested since the screen_eltas.csv would make both the iOS and Android data comparable These files are used to calculate the features for the screen sensor

• The screen delta data file contains data for 1 day.
• The screen delta data contains 1 record to represent an unlock episode that falls within an epoch for every epoch.
• The screen delta data contains 1 record to represent an unlock episode that falls across the boundary of 2 epochs. Namely the unlock episode starts in one epoch and ends in the next, thus there is a record for unlock episodes that fall across night to morning, morning to afternoon and finally afternoon to night
• The testing is done for unlock episode_type.
• There is one screen data file each for testing both iOS and Android data formats.
• There is also an additional empty data file for both android and iOS for testing empty data files

Battery

Due to the difference in the format of the raw battery data for iOS and Android as well as versions of iOS the following is the expected results the battery_deltas.csv. This would give a better idea of the use cases being tested since the battery_deltas.csv would make both the iOS and Android data comparable. These files are used to calculate the features for the battery sensor.

• The battery delta data file contains data for 1 day.
• The battery delta data contains 1 record each for a charging and discharging episode that falls within an epoch for every epoch. Thus, for the daily epoch there would be multiple charging and discharging episodes
• Since either a charging episode or a discharging episode and not both can occur across epochs, in order to test episodes that occur across epochs alternating episodes of charging and discharging episodes that fall across night to morning, morning to afternoon and finally afternoon to night are present in the battery delta data. This starts with a discharging episode that begins in night and end in morning.
• There is one battery data file each, for testing both iOS and Android data formats.
• There is also an additional empty data file for both android and iOS for testing empty data files

Bluetooth

• The raw Bluetooth data file contains data for 1 day.
• The raw Bluetooth data contains at least 2 records for each epoch. Each epoch has a record with a timestamp for the beginning boundary for that epoch and a record with a timestamp for the ending boundary for that epoch. (e.g. For the morning epoch there is a record with a timestamp for 6:00AM and another record with a timestamp for 11:59:59AM. These are to test edge cases)
• An option of 5 Bluetooth devices are randomly distributed throughout the data records.
• There is one raw Bluetooth data file each, for testing both iOS and Android data formats.
• There is also an additional empty data file for both android and iOS for testing empty data files.

WIFI

• There are 2 data files (wifi_raw.csv and sensor_wifi_raw.csv) for each fake participant for each phone platform.
• The raw WIFI data files contain data for 1 day.
• The sensor_wifi_raw.csv data contains at least 2 records for each epoch. Each epoch has a record with a timestamp for the beginning boundary for that epoch and a record with a timestamp for the ending boundary for that epoch. (e.g. For the morning epoch there is a record with a timestamp for 6:00AM and another record with a timestamp for 11:59:59AM. These are to test edge cases)
• The wifi_raw.csv data contains 3 records with random timestamps for each epoch to represent visible broadcasting WIFI network. This file is empty for the iOS phone testing data.
• An option of 10 access point devices is randomly distributed throughout the data records. 5 each for sensor_wifi_raw.csv and wifi_raw.csv.
• There data files for testing both iOS and Android data formats.
• There are also additional empty data files for both android and iOS for testing empty data files.

Light

• The raw light data file contains data for 1 day.
• The raw light data contains 3 or 4 rows of data for each epoch except night. The single row of data for night is for testing features for single values inputs. (Example testing the standard deviation of one input value)
• Since light is only available for Android there is only one file that contains data for Android. All other files (i.e. for iPhone) are empty data files.

Locations

Description

• The participant's home location is (latitude=1, longitude=1).
• From Sat 10:56:00 to Sat 11:04:00, the center of the cluster is (latitude=-100, longitude=-100).
• From Sun 03:30:00 to Sun 03:47:00, the center of the cluster is (latitude=1, longitude=1). Home location is extracted from this period.
• From Sun 11:30:00 to Sun 11:38:00, the center of the cluster is (latitude=100, longitude=100).

Application Foreground

• The raw application foreground data file contains data for 1 day.
• The raw application foreground data contains 7 - 9 rows of data for each epoch. The records for each epoch contains apps that are randomly selected from a list of apps that are from the MULTIPLE_CATEGORIES and SINGLE_CATEGORIES (See testing_config.yaml). There are also records in each epoch that have apps randomly selected from a list of apps that are from the EXCLUDED_CATEGORIES and EXCLUDED_APPS. This is to test that these apps are actually being excluded from the calculations of features. There are also records to test SINGLE_APPS calculations.
• Since application foreground is only available for Android there is only one file that contains data for Android. All other files (i.e. for iPhone) are empty data files.

Activity Recognition

• The raw Activity Recognition data file contains data for 1 day.
• The raw Activity Recognition data each epoch period contains rows that records 2 - 5 different activity_types. The is such that durations of activities can be tested. Additionally, there are records that mimic the duration of an activity over the time boundary of neighboring epochs. (For example, there a set of records that mimic the participant in_vehicle from afternoon into evening)
• There is one file each with raw Activity Recognition data for testing both iOS and Android data formats. (plugin_google_activity_recognition_raw.csv for android and plugin_ios_activity_recognition_raw.csv for iOS)
• There is also an additional empty data file for both android and iOS for testing empty data files.

Conversation

• The raw conversation data file contains data for 2 day.
• The raw conversation data contains records with a sample of both datatypes (i.e. voice/noise = 0, and conversation = 2 ) as well as rows with for samples of each of the inference values (i.e. silence = 0, noise = 1, voice = 2, and unknown = 3) for each epoch. The different datatype and inference records are randomly distributed throughout the epoch.
• Additionally there are 2 - 5 records for conversations (datatype = 2, and inference = -1) in each epoch and for each epoch except night, there is a conversation record that has a double_convo_start timestamp that is from the previous epoch. This is to test the calculations of features across epochs.
• There is a raw conversation data file for both android and iOS platforms (plugin_studentlife_audio_android_raw.csv and plugin_studentlife_audio_raw.csv respectively).
• Finally, there are also additional empty data files for both android and iOS for testing empty data files

Keyboard

• The raw keyboard data file contains data for 4 days.

• The raw keyboard data contains records with difference in timestamp ranging from milliseconds to seconds.

• With difference in timestamps between consecutive records more than 5 seconds helps us to create separate sessions within the usage of the same app. This helps to verify the case where sessions have to be different.

• The raw keyboard data contains records where the difference in text is less than 5 seconds which makes it into 1 session but because of difference of app new session starts. This edge case determines the behaviour within particular app and also within 5 seconds.

• The raw keyboard data also contains the records where length of current_text varies between consecutive rows. This helps us to tests on the cases where input text is entered by auto-suggested or auto-correct operations.

• One three-minute episode with a 1-minute row on Sun 08:59:54.65 and 09:00:00,another on Sun 12:01:02 that are considering a single episode in multi-timezone event segments to showcase how inferring time zone data for Keyboard from phone data can produce inaccurate results around the tz change. This happens because the device was on LA time until 11:59 and switched to NY time at 12pm, in terms of actual time 09 am LA and 12 pm NY represent the same moment in time so 09:00 LA and 12:01 NY are consecutive minutes.

Fitbit Calories Intraday

Description

• A five-minute sedentary episode on Fri 11:00:00
• A one-minute sedentary episode on Sun 02:00:00. It exists in November but not in February in STZ
• A five-minute sedentary episode on Fri 11:58:00. It is split within two 30-min segments and the morning
• A three-minute lightly active episode on Fri 11:10:00, a one-minute at 11:18:00 and a one-minute 11:24:00. These check for start and end times of first/last/longest episode
• A three-minute fairly active episode on Fri 11:40:00, a one-minute at 11:48:00 and a one-minute 11:54:00. These check for start and end times of first/last/longest episode
• A three-minute very active episode on Fri 12:10:00, a one-minute at 12:18:00 and a one-minute 12:24:00. These check for start and end times of first/last/longest episode
• A eight-minute MVPA episode with intertwined fairly and very active rows on Fri 12:30:00
• The above episodes contain six higmet (>= 3 MET) episodes and nine lowmet episodes.
• One two-minute sedentary episode with a 1-minute row on Sun 09:00:00 and another on Sun 12:01:01 that are considering a single episode in multi-timezone event segments to showcase how inferring time zone data for Fitbit from phone data can produce inaccurate results around the tz change. This happens because the device was on LA time until 11:59 and switched to NY time at 12pm, in terms of actual time 09 am LA and 12 pm NY represent the same moment in time so 09:00 LA and 12:01 NY are consecutive minutes.
• A three-minute sedentary episode on Sat 08:59 that will be ignored for multi-timezone event segments.
• A three-minute sedentary episode on Sat 12:59 of which the first minute will be ignored for multi-timezone event segments since the test segment starts at 13:00
• A three-minute sedentary episode on Sat 16:00
• A four-minute sedentary episode on Sun 10:01 that will be ignored for Novembers's multi-timezone event segments since the test segment ends at 10am on that weekend.
• A three-minute very active episode on Sat 16:03. This episode and the one at 16:00 are counted as one for lowmet episodes

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Sleep Summary

Description

• A main sleep episode that starts on Fri 20:00:00 and ends on Sat 02:00:00. This episode starts after 11am (Last Night End) which will be considered as today's (Fri) data.
• A nap that starts on Sat 04:00:00 and ends on Sat 06:00:00. This episode starts before 11am (Last Night End) which will be considered as yesterday's (Fri) data.
• A nap that starts on Sat 13:00:00 and ends on Sat 15:00:00. This episode starts after 11am (Last Night End) which will be considered as today's (Sat) data.
• A main sleep that starts on Sun 01:00:00 and ends on Sun 12:00:00. This episode starts before 11am (Last Night End) which will be considered as yesterday's (Sat) data.
• A main sleep that starts on Sun 23:00:00 and ends on Mon 07:00:00. This episode starts after 11am (Last Night End) which will be considered as today's (Sun) data.
• Any segment shorter than one day will be ignored for sleep RAPIDS features.

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Sleep Intraday

Description

• A five-minute main sleep episode with asleep-classic level on Fri 11:00:00.
• An eight-hour main sleep episode on Fri 17:00:00. It is split into 2 parts for daily segment: a seven-hour sleep episode on Fri 17:00:00 and an one-hour sleep episode on Sat 00:00:00.
• A two-hour nap on Sat 01:00:00 that will be ignored for main sleep features.
• An one-hour nap on Sat 13:00:00 that will be ignored for main sleep features.
• An eight-hour main sleep episode on Sat 22:00:00. This episode ends on Sun 08:00:00 (NY) for March and Sun 06:00:00 (NY) for Novembers due to daylight savings. It will be considered for beforeMarchEvent segment and ignored for beforeNovemberEvent segment.
• A nine-hour main sleep episode on Sun 11:00:00. Start time will be assigned as NY time zone and converted to 14:00:00.
• A seven-hour main sleep episode on Mon 06:00:00. This episode will be split into two parts: a five-hour sleep episode on Mon 06:00:00 and a two-hour sleep episode on Mon 11:00:00. The first part will be discarded as it is before 11am (Last Night End)
• Any segment shorter than one day will be ignored for sleep PRICE features.

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit