App stores allow users to give valuable feedback on apps, and developers to find this feedback and use it for the software evolution. However, finding user feedback that matches existing bug reports in issue trackers is challenging as users and developers often use a different language. In this work, we introduce DeepMatcher, an automatic approach using state-of-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluated DeepMatcher with four open-source apps quantitatively and qualitatively. On average, DeepMatcher achieved a hit ratio of 0.71 and a Mean Average Precision of 0.55. For 91 problem reports, DeepMatcher did not find any matching bug report. When manually analyzing these 91 problem reports and the issue trackers of the studied apps, we found that in 47 cases, users actually described a problem before developers discovered and documented it in the issue tracker. We discuss our findings and different use cases for DeepMatcher.

Preclinical patient care is both mentally and physically challenging and exhausting for emergency teams. The teams intensively use medical technology to help the patient on site. However, they must carry and handle multiple heavy medical devices such as a monitor for the patient's vital signs, a ventilator to support an unconscious patient, and a resuscitation device. In an industry project, we aim at developing a combined device that lowers the emergency teams' mental and physical load caused by multiple screens, devices, and their high weight. The focus of this paper is to describe our ideation and requirements elicitation process regarding the user interface design of the combined device. For one year, we applied a fully digital customized version of the Applied Cognitive Task Analysis (ACTA) method to systematically elicit the requirements. Domain and requirements engineering experts created a detailed hierarchical task diagram of an extensive emergency scenario, conducted eleven interviews with subject matter experts (SMEs), and executed two design workshops, which led to 34 sketches and three mockups of the combined device's user interface. Cross-functional teams accompanied the entire process and brought together expertise in preclinical patient care, requirements engineering, and medical product development. We report on the lessons learned for each of the four consecutive stages of our customized ACTA process.

In the dynamic and fast-growing app market, monitoring and understanding how past releases are actually being used is indispensable for successful app maintenance and evolution. Current app usage analytics tools either log execution events, e.g., in stack traces, or general usage information such as the app activation time, location, and device. In this paper, we focus on analyzing the usages of the single app features as described in release notes and app pages. We suggest monitoring nine app-independent, privacy-friendly interaction events for training a machine learning model to learn app feature usages. We conducted a crowdsourcing study with 55 participants who labeled 5,815 feature usages of 170 unique apps for 18 days. Our within-apps evaluation shows that we could achieve encouraging precision and recall values already with ten labeled feature usages. For certain popular features such as browse newsfeed or send an email, we achieved F1 values above 88%. Betweenapps feature learning seems feasible with F1 values of up to 86%.

With the rise of social media like Twitter and distribution platforms like app stores, users have various ways to express their opinions about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams. However, a manual analysis of user feedback is cumbersome and hard to manage. We present OpenReq Analytics, a software requirements intelligence service, that collects, processes, analyzes, and visualizes user feedback.

Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.

A main advantage of app stores is that they aggregate important information created by both developers and users. In the app store product pages, developers usually describe and maintain the features of their apps. In the app reviews, users comment these features. Recent studies focused on mining app features either as described by developers or as reviewed by users. However, extracting and matching the features from the app descriptions and the reviews is essential to bear the app store advantages, e.g. allowing analysts to identify which app features are actually being reviewed and which are not. In this paper, we propose SAFE, a novel uniform approach to extract app features from the single app pages, the single reviews and to match them. We manually build 18 part-of-speech patterns and 5 sentence patterns that are frequently used in text referring to app features. We then apply these patterns with several text pre-and post-processing steps. A major advantage of our approach is that it does not require large training and configuration data. To evaluate its accuracy, we manually extracted the features mentioned in the pages and reviews of 10 apps. The extraction precision and recall outperformed two state-of-the-art approaches. For well-maintained app pages such as for Google Drive our approach has a precision of 87% and on average 56% for 10 evaluated apps. SAFE also matches 87% of the features extracted from user reviews to those extracted from the app descriptions.