Search Results for author: Michael Felderer

Found 17 papers, 1 papers with code

A survey on software testability

no code implementations7 Jan 2018 Vahid Garousi, Michael Felderer, Feyza Nur Kilicaslan

Results: The area of software testability has been comprehensively studied by researchers and practitioners.

Software Engineering

NLP-assisted software testing: A systematic mapping of the literature

no code implementations2 Jun 2018 Vahid Garousi, Sara Bauer, Michael Felderer

After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers.

Specification-Driven Predictive Business Process Monitoring

no code implementations20 Apr 2019 Ario Santoso, Michael Felderer

Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks.

Technical Debt and Waste in Non-Functional Requirements Documentation: An Exploratory Study

no code implementations27 Sep 2019 Gabriela Robiolo, Ezequiel Scott, Santiago Matalonga, Michael Felderer

Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs documentation.

Software Engineering

Controlled Experimentation in Continuous Experimentation: Knowledge and Challenges

no code implementations10 Feb 2021 Florian Auer, Rasmus Ros, Lukas Kaltenbrunner, Per Runeson, Michael Felderer

Finally, we were interested in the challenges and benefits reported of continuous experimentation.

Software Engineering

Compliance Requirements in Large-Scale Software Development: An Industrial Case Study

no code implementations2 Mar 2021 Muhammad Usman, Michael Felderer, Michael Unterkalmsteiner, Eriks Klotins, Daniel Mendez, Emil Alegroth

Regulatory compliance is a well-studied area, including research on how to model, check, analyse, enact, and verify compliance of software.

Software Engineering

Towards Risk Modeling for Collaborative AI

no code implementations12 Mar 2021 Matteo Camilli, Michael Felderer, Andrea Giusti, Dominik T. Matt, Anna Perini, Barbara Russo, Angelo Susi

Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance.

BIG-bench Machine Learning

What is Software Quality for AI Engineers? Towards a Thinning of the Fog

no code implementations23 Mar 2022 Valentina Golendukhina, Valentina Lenarduzzi, Michael Felderer

Thus, the goal of this study is to investigate the software quality assurance strategies adopted during the development, integration, and maintenance of AI/ML components and code.

Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA

no code implementations16 May 2022 Neetha Jambigi, Thomas Bach, Felix Schabernack, Michael Felderer

However, we experienced that such replays can produce a large amount of false positive alerts that make the results unreliable or time consuming to analyze.

Guiding the retraining of convolutional neural networks against adversarial inputs

no code implementations8 Jul 2022 Francisco Durán López, Silverio Martínez-Fernández, Michael Felderer, Xavier Franch

Our goal is to improve the models against adversarial inputs regarding accuracy, resource utilization and time from the point of view of a data scientist in the context of image classification.

Image Classification

The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice

no code implementations21 Jan 2023 Monika Steidl, Michael Felderer, Rudolf Ramler

To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required.

Management

VULNERLIZER: Cross-analysis Between Vulnerabilities and Software Libraries

no code implementations18 Sep 2023 Irdin Pekaric, Michael Felderer, Philipp Steinmüller

In this paper, we present VULNERLIZER, which is a novel framework for cross-analysis between vulnerabilities and software libraries.

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