Project

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SURF-MobileAppsData

English title SURF-MobileAppsData
Applicant Gall Harald
Number 166275
Funding scheme Project funding (Div. I-III)
Research institution Institut für Informatik Universität Zürich
Institution of higher education University of Zurich - ZH
Main discipline Information Technology
Start/End 01.09.2016 - 31.03.2021
Approved amount 471'018.00
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Keywords (4)

software mining; software evolution; software engineering; mobile apps

Lay Summary (German)

Lead
Mit Mobile Apps bezeichnet man Anwendungssoftware für mobile Endgeräte wie Smartphones oder Tablets. Solche Apps werden für interessierte Benutzer in sogenannten App Stores zum Herunterladen und Benützen bereitgestellt. Benutzer bewerten und kommentieren diese Apps für andere Interessierte. Diese Daten sind sehr wertvoll, um diese Apps software-technisch weiterzuentwickeln, mit neuen Funktionen auszustatten oder zu verbessern in deren Qualität. Das vorliegende Projekt will diese Daten analysieren und aufbereiten und mit der Entwicklung eines Rahmenwerks dazu beitragen, dass diese App von Software-Entwicklern besser und effizienter weiterentwickelt werden können.
Lay summary

Inhalt und Ziel

Das SURF-MobileAppsData Projekt analysiert mittels Data Mining die Benutzerdaten vonMobile Apps in App Stores. Mit dieser Datenanalyse soll die Weiterentwicklung, Verbesserung und Optimierung der Apps beigetragen werden. Im Projekt soll dazu ein Rahmenwerk entwickelt werden, dass eine solche Feedback-getriebene Software-Entwicklung effektiv unterstützt. Benutzerwünsche und Bewertungen bergen ein enormes Potential für eine zielführende Verbesserung von Apps hinsichtlich Funktionalität, Markt, oder im Vergleich zu konkurrenzierenden Apps. Durch die Erforschung und Entwicklung diverser Datenquellen zu Mobile Apps können Benutzerwünsche besser analysiert und umgesetzt werden. Die Integration von diesen diversen Datenquellen stellt eine grosse Herausforderung und Chance dar, da es bis dato noch keine geeigneten Analysen dieser gehaltvollen Daten in einer systematischen Art und Weise gibt. 

Wissenschaftlicher und gesellschaftlicher Kontext

Mit diesem Projekt sollen die Benutzer aktiv in den Entwicklungs- und Verbesserungsprozess von Mobile Apps eingebunden werden. Damit erhöht sich der Nutzen von Bewertungen und Kommentaren zu solchen Apps substantiell, sodass ein Mehrwert für beide Seiten - Nutzer wie Entwickler - generiert werden kann.  

Direct link to Lay Summary Last update: 28.06.2016

Responsible applicant and co-applicants

Employees

Publications

Publication
Lightweight Assessment of Test-Case Effectiveness Using Source-Code-Quality Indicators
Grano Giovanni, Palomba Fabio, Gall Harald C. (2021), Lightweight Assessment of Test-Case Effectiveness Using Source-Code-Quality Indicators, in IEEE Transactions on Software Engineering, 47(4), 758-774.
Every build you break: developer-oriented assistance for build failure resolution
Vassallo Carmine, Proksch Sebastian, Zemp Timothy, Gall Harald C. (2020), Every build you break: developer-oriented assistance for build failure resolution, in Empirical Software Engineering, 25(3), 2218-2257.
How developers engage with static analysis tools in different contexts
Vassallo Carmine, Panichella Sebastiano, Palomba Fabio, Proksch Sebastian, Gall Harald C., Zaidman Andy (2020), How developers engage with static analysis tools in different contexts, in Empirical Software Engineering, 25(2), 1419-1457.
Branch coverage prediction in automated testing
Grano Giovanni, Titov Timofey V., Panichella Sebastiano, Gall Harald C. (2019), Branch coverage prediction in automated testing, in Journal on Software Evolution and Processes, 31(9), 1-18.
Automated reporting of anti-patterns and decay in continuous integration
Vassallo Carmine, Proksch Sebastian, Gall Harald C., Penta Massimiliano Di (2019), Automated reporting of anti-patterns and decay in continuous integration, in Proceedings of the 41st Int Conf on Software Engineering, ICSE 2019, 105-115, IEEE / ACM, New York105-115.
On the effectiveness of manual and automatic unit test generation: ten years later
Serra Domenico, Grano Giovanni, Palomba Fabio, Ferrucci Filomena, Gall Harald C., Bacchelli Alberto (2019), On the effectiveness of manual and automatic unit test generation: ten years later, in Proceedings of the 16th Int Conf on Mining Software Repositories, MSR 2019, 121-125, IEEE / ACM, New York121-125.
A large-scale empirical exploration on refactoring activities in open source software projects
Vassallo Carmine, Grano Giovanni, Palomba Fabio, Gall Harald C., Bacchelli Alberto (2019), A large-scale empirical exploration on refactoring activities in open source software projects, in Sci. Comput. Program., 180, 1-15.
Scented since the beginning: On the diffuseness of test smells in automatically generated test code
Grano Giovanni, Palomba Fabio, Nucci Dario Di, Lucia Andrea De, Gall Harald C. (2019), Scented since the beginning: On the diffuseness of test smells in automatically generated test code, in Journal on Systems and Software, 156, 312-327.
Continuous code quality: are we (really) doing that?
Vassallo Carmine, Palomba Fabio, Bacchelli Alberto, Gall Harald C. (2018), Continuous code quality: are we (really) doing that?, in Proceedings of the 33rd ACM/IEEE Int'l Conf on Automated Software Engineering, ASE 2018, 790-795, ACM, New York790-795.
Continuous Refactoring in CI: A Preliminary Study on the Perceived Advantages and Barriers
Vassallo Carmine, Palomba Fabio, Gall Harald C. (2018), Continuous Refactoring in CI: A Preliminary Study on the Perceived Advantages and Barriers, in 2018 Int Conf on Software Maintenance and Evolution, ICSME 2018, 564-568, IEEE Computer Society, Washington, DC564-568.
OCELOT: a search-based test-data generation tool for C
Scalabrino Simone, Grano Giovanni, Nucci Dario Di, Guerra Michele, Lucia Andrea De, Gall Harald C., Oliveto Rocco (2018), OCELOT: a search-based test-data generation tool for C, in Proceedings of the 33rd Int Conf on Automated Software Engineering, ASE 2018, 868-871, ACM, New York868-871.
An empirical investigation on the readability of manual and generated test cases
Grano Giovanni, Scalabrino Simone, Gall Harald C., Oliveto Rocco (2018), An empirical investigation on the readability of manual and generated test cases, in Proceedings of the 26th Conf on Program Comprehension, ICPC 2018, 348-351, ACM, New York348-351.
Un-break my build: assisting developers with build repair hints
Vassallo Carmine, Proksch Sebastian, Zemp Timothy, Gall Harald C. (2018), Un-break my build: assisting developers with build repair hints, in Proceedings of the 26th Conf on Program Comprehension, ICPC 2018, 41-51, ACM, New York41-51.
BECLoMA: Augmenting stack traces with user review information
Pelloni Lucas, Grano Giovanni, Ciurumelea Adelina, Panichella Sebastiano, Palomba Fabio, Gall Harald C. (2018), BECLoMA: Augmenting stack traces with user review information, in 25th Int Conf on Software Analysis, Evolution and Reengineering, SANER 2018, 522-526, IEEE Computer Society, Washington, DC522-526.
Context is king: The developer perspective on the usage of static analysis tools
Vassallo Carmine, Panichella Sebastiano, Palomba Fabio, Proksch Sebastian, Zaidman Andy, Gall Harald C. (2018), Context is king: The developer perspective on the usage of static analysis tools, in 25th Int Conf on Software Analysis, Evolution and Reengineering, SANER 2018, 38-49, IEEE Computer Society, Washington, DC38-49.
Exploring the integration of user feedback in automated testing of Android applications
Grano Giovanni, Ciurumelea Adelina, Panichella Sebastiano, Palomba Fabio, Gall Harald C. (2018), Exploring the integration of user feedback in automated testing of Android applications, in 25th Int Conf on Software Analysis, Evolution and Reengineering, SANER 2018, 72-83, IEEE Computer Society, Washington, DC72-83.
How high will it be? Using machine learning models to predict branch coverage in automated testing
Grano Giovanni, Titov Timofey V., Panichella Sebastiano, Gall Harald C. (2018), How high will it be? Using machine learning models to predict branch coverage in automated testing, in Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE 2018, 19-24, IEEE Computer Society, Washington, DC19-24.
Data-Driven Decisions and Actions in Today's Software Development
Gall Harald C., Alexandru Carol V., Ciurumelea Adelina, Grano Giovanni, Laaber Christoph, Panichella Sebastiano, Proksch Sebastian, Schermann Gerald, Vassallo Carmine, Zhao Jitong (2018), Data-Driven Decisions and Actions in Today's Software Development, in Striemer Rüdiger, Gruhn Volker (ed.), Springer, Cham, 137-168.

Awards

Title Year
Best Tool Demo paper entitled "BECLoMA: Augmenting Stack Traces with User Review Information" at SANER 2018 conference - see publications 2018

Abstract

The SURF-MobileAppsData project will mine data about mobile apps that are available in app stores to support software engineers in the maintenance and evolution of these apps. In particular, the goal is to devise an analysis framework and a feedback-driven environment to help developers to shorten the development life cycle and to accommodate actual user needs. Hence, the main purpose of the project is to surf the large amount of data (such as user reviews or app ratings etc.) that characterizes any app in an app store with the aim of advancing the current state-of-the-art in mining mobile apps in several novel directions: by providing a mulit-level, multi-source feedback mechanism for developers and users; by devising means for multi-source interlinking of user requests and actual changes; and by better wiring up feature development and bug fixing thru user feedbacks.
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