Data-Driven Requirements Engineering - An Update

Nowadays, users can easily submit feedback about software products in app stores, social media, or user groups. Moreover, software vendors are collecting massive amounts of implicit feedback in the form of usage data, error logs, and sensor data. These trends suggest a shift toward data-driven user-centered identification, prioritization, and management of software requirements. Developers should be able to adopt the requirements of masses of users when deciding what to develop and when to release. They could systematically use explicit and implicit user data in an aggregated form to support requirements decisions. In this talk we will present and discuss most recent achievements in this direction since the paper's original publication. We will also show to mine data sets mobile apps, give a few success/failure stories and a few practical advises.

[1]  Xavier Franch,et al.  How Can Quality Awareness Support Rapid Software Development? - A Research Preview , 2017, REFSQ.

[2]  Maleknaz Nayebi,et al.  Optimized Functionality for Super Mobile Apps , 2017, 2017 IEEE 25th International Requirements Engineering Conference (RE).

[3]  Walid Maalej,et al.  How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews , 2014, 2014 IEEE 22nd International Requirements Engineering Conference (RE).

[4]  Maleknaz Nayebi,et al.  Toward Data-Driven Requirements Engineering , 2016, IEEE Software.

[5]  Walid Maalej,et al.  On user rationale in software engineering , 2018, Requirements Engineering.

[6]  Thomas Zimmermann,et al.  What Makes a Good Bug Report? , 2008, IEEE Transactions on Software Engineering.

[7]  Per Runeson,et al.  Open innovation in software engineering: a systematic mapping study , 2015, Empirical Software Engineering.

[8]  Walid Maalej,et al.  SAFE: A Simple Approach for Feature Extraction from App Descriptions and App Reviews , 2017, 2017 IEEE 25th International Requirements Engineering Conference (RE).

[9]  Maleknaz Nayebi,et al.  App store mining is not enough for app improvement , 2018, Empirical Software Engineering.

[10]  Yuanyuan Zhang,et al.  A Survey of App Store Analysis for Software Engineering , 2017, IEEE Transactions on Software Engineering.

[11]  Walid Maalej,et al.  User feedback in the appstore: An empirical study , 2013, 2013 21st IEEE International Requirements Engineering Conference (RE).

[12]  Maleknaz Nayebi,et al.  Which Version Should Be Released to App Store? , 2017, 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM).

[13]  Claes Wohlin,et al.  The fundamental nature of requirements engineering activities as a decision-making process , 2003, Inf. Softw. Technol..