Search · four archives
Search · four archives
17 papers · ranked by Valyu relevance
Ayesha Kiran, Wasi Haider Butt, Arslan Shaukat, Muhammad Umar Farooq + 4 more
'Urooj Fatima' 'Farooque Azam' 'Zeeshan Anwar' 'Dragan Pamucar'] In the process of software development, regression testing is one of the major activities that is done after making modifications in the current system or whenever a software system evolves. But, the test suite size increases with the addition of new test…
Antonio Trovato, Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci + 1 more
-- an Empirical Study Authors: ['Antonio Trovato' 'Manuel De Stefano' 'Fabiano Pecorelli' 'Dario Di Nucci' 'Andrea De Lucia'] Maintaining software quality is crucial in the dynamic landscape of software development. Regression testing ensures that software works as expected after changes are implemented. However…
Dario Amoroso d'Aragona, Fabiano Pecorelli, Simone Romano, Giuseppe Scanniello + 3 more
'Giuseppe Scanniello' 'María Teresa Baldassarre' 'Andrea Janes' 'Valentina Lenarduzzi'] Abstract—Regression testing wants to prevent that errors, which have already been corrected once, creep back into a system that has been updated. A na¨ıve approach consists of re-running the entire test suite (TS) against the…
Daniel Schwendner, Maximilian Jungwirth, Martin Gruber, Martin Knoche + 2 more
Integration Authors: ['Daniel Schwendner' 'Maximilian Jungwirth' 'Martin Gruber' 'Martin Knoche' 'Daniel Merget' 'Gordon Fraser'] Abstract—Massive, multi-language, monolithic repositories form the backbone of many modern, complex software systems. To ensure consistent code quality while still allowing fast development…
Anu Bajaj, Ajith Abraham, Saroj Ratnoo, Lubna Abdelkareim Gabralla + 1 more
The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before…
Sebastian Ruland, Malte Lochau
Unit testing is one of the most established quality-assurance techniques for software development. One major advantage of unit testing is the adjustable trade-off between efficiency (i.e., testing effort) and effectiveness (i.e., fault-detection probability). To this end, various strategies have been proposed to…
Deepak Narayan Gadde, Sebastian Simon, Djones Lettnin, Thomas Ziller
— The verification throughput is becoming a major challenge bottleneck, since the complexity and size of SoC designs are still ever increasing. Simply adding more CPU cores and running more tests in parallel will not scale anymore. This paper discusses various methods of improving verification throughput: ranking and…
Lukas Forer, Sebastian Schönherr
The workflow management system Nextflow builds together with the nf-core community an essential ecosystem in Bioinformatics. However, ensuring the correctness and reliability of large and complex pipelines is challenging, since a unified and automated unit-style testing framework specific to Nextflow is still missing.…
Christoph Laaber, Harald C. Gall, Philipp Leitner
Regression testing comprises techniques which are applied during software evolution to uncover faults effectively and efficiently. While regression testing is widely studied for functional tests, performance regression testing, e.g., with software microbenchmarks, is hardly investigated. Applying test case…
Martin Grambow, Christoph Laaber, Philipp Leitner, David Bermbach + 1 more
'Muhammad Aleem'] Performance problems in applications should ideally be detected as soon as they occur, i.e., directly when the causing code modification is added to the code repository. To this end, complex and cost-intensive application benchmarks or lightweight but less relevant microbenchmarks can be added to…
Haley Hunter-Zinck, Alexandre Fioravante de Siqueira, Váleri N. Vásquez, Richard Barnes + 2 more
'Váleri N. Vásquez' 'Richard Barnes' 'Ciera C. Martinez' 'Scott Markel'] Functional, usable, and maintainable open-source software is increasingly essential to scientific research, but there is a large variation in formal training for software development and maintainability. Here, we propose 10 “rules” centered on 2…
Daniel Svensson, Rickard Sjögren, David Sundell, Andreas Sjödin + 1 more
Selecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex…
Sterling G. Baird, Jeet N. Parikh, Taylor D. Sparks
Benchmarks are crucial for driving progress in scientific disciplines. To be effective, benchmarks should closely mimic real-world tasks while being computationally efficient, allowing for accessibility and repeatability. Developing surrogate models that can be indistinguishable from the ground truth observation within…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Authors not listed
This paper presents the Multi Cell-line Kinetic Model (MCKM), a novel generalised kinetic mechanistic model specifically tailored for Ambr15™ fed-batch cultivations of multiple Chinese Hamster Ovary (CHO) cell lines producing different recombinant monoclonal antibodies (mAbs). Unlike traditional models that requires…
Christian Lieven, Moritz E. Beber, Brett G. Olivier, Frank T. Bergmann + 62 more
Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we…
Vishal Bharti, Debojyoti Chakraborty
Reproducible computational biology depends on statistical decisions that routine workflows often skip: verifying that a differential-expression test’s assumptions hold across all genes, that a strategy-comparison ANOVA is robust to non-normality, or that a meta-analysis is not distorted by publication bias. Surveys…