REQUIREMENTS INTELLIGENCE ENGINE. MS ri-storage-app This microservice is the interface to the actual database and persists JSON objects.
REQUIREMENTS INTELLIGENCE ENGINE. This section presents and overview of the Requirements Intelligence Engine as well as a description of the available APIs.
REQUIREMENTS INTELLIGENCE ENGINE. MS analytics backend This microservice performs topic extraction of the tweets addressed to a specified Twitter account. The microservice identifies the topics of major interest and what a large amount of tweets talk about. This gives the possibility to pinpoint inconveniences, system failures, dissatisfaction, or customer’s necessities.
REQUIREMENTS INTELLIGENCE ENGINE. MS ri-analytics-classification-google-play-review The goal of this microservice is to classify a list of app reviews as either a “bug report” or a “feature request.” The source code necessary for these tasks is bundled in a Docker container. Each task is composed of sub-tasks. Such sub-tasks are data cleaning, machine learning feature extraction, and classification on pre-trained models. The response of the microservice is a list of app reviews that now include the class they belong to.
REQUIREMENTS INTELLIGENCE ENGINE. MS ri-analytics-classification-twitter The goal of this microservice is to take a list of tweets, extract their natural language features and classify them either as a problem report, inquiry, or irrelevant.
REQUIREMENTS INTELLIGENCE ENGINE. MS ri-analytics-rationale-miner When users write reviews they often also mention their rationale of why, e.g., they chose an alternative to the software under review or why a certain decision is taken. This microservice extracts the probability of a user rationale existing in an Amazon review. The microservice supports the user rationale categories decision, alternative, justification, and criteria being part of an Amazon review.
REQUIREMENTS INTELLIGENCE ENGINE. MS xx-collection-explicit-feedback-google-play-page The goal of this microservice is to collect data from the Google Play Store—the official store for Android apps. In particular, this service collects the data available on the page representing the app. The response contains information of the app page in JSON format.
REQUIREMENTS INTELLIGENCE ENGINE. MS xx-collection-explicit-feedback-twitter The goal of this microservice is to collect data from Twitter. In particular, this service collects tweets that mention a given account. The response contains a list of tweets in a JSON format.
REQUIREMENTS INTELLIGENCE ENGINE. MS ri-logging This microservice collects implicit feedback from user interactions with OpenReq UIs and user/microservice interactions with the backend. The UI interactions are captured by a Javascript library that only needs to be imported by the UI. No integration inside the UIs code (apart from <script src=“<url>“></script>) is needed. The events in the UI are sent to this logger microservices API and saved in a text file or database. The logs are accessible through this microservices API. The backend interaction is captured by a component (not part of the microservice) of Ngix. Every request and response that reaches the backend (i.e., all OpenReq microservices) is logged in a file. The log file is accessible through an API. The access to the log files is restricted to owners of a bearer token.
REQUIREMENTS INTELLIGENCE ENGINE. MS ri-orchestration-app This is the orchestration microservice responsible to coordinate all microservices for app store data analysis. The goal is to simplify the access to the diverse microservices and to coordinate them to achieve a certain goal. The main goal is to define apps that should continuously be observed by OpenReq. In a given interval, the apps and their user reviews are crawled, then classified, and finally stored in the database.
