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About 10,000 papers for “Software quality control”

Proteomics Quality Control: Quality Control Software for MaxQuant Results

Chris Bielow, Guido Mastrobuoni, Stefan Kempa · Journal of Proteome Research · 2015

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC .

Cited by 214Open access

Modeling software quality control processes

Каринэ Хачересовна Калугян, Капитолина Щербакова, Марина Глущенко et al. · Open MIND · 2020

Рассматриваются вопросы осуществления контроля качества разрабатываемого программного обеспечения путем проведения тестирования. Для визуального моделирования процессов тестирования используется язык UML.

Cited by 0

From Software Quality Control to Quality Assurance

2008

This paper addresses the transformation of traditional software quality control techniques to a proactive quality assurance program within embedded systems and other mission critical software: Software plays an increasingly important role in every product and organization. The number of mission critical applications, those with a high cost of failure (air traffic control systems and other life support products), or a high cost to fix (cellular phones and other consumer products), have increased exponentially in recent years. Software for embedded systems more often than not fits this profile of “mission critical, ” and with the forecast for embedded systems continuing to accelerate, the need for proactive quality assurance is higher than ever before. This paper previews a two-stage approach to transforming quality control to quality assurance: • Quality Assessment (Stage 1) provides objective quantitative evaluation of quality while identifying and fixing software defects early in the development cycle, before they get to the customer and even before testing; • Quality Assurance (Stage 2) helps prevent defects from entering the code base by enforcing coding standards during, and by preventing new defects from occurring during times of change through accurate impact analysis. 1

Cited by 0

Using statistical control charts for software quality control

John S. Gardiner, Douglas C. Montgomery · Quality and Reliability Engineering International · 1987

Abstract This paper describes the use of Shewhart U charts for controlling software quality while the software is under development. A parallel between the software development process and a complex manufacturing process is developed. This allows the use of statistical control charts to monitor the software development process. The general approach is to let observed defect data represent a sample of the true but unknown state of the entire software project. Software quality data so obtained can be used to derive a meaningful statistical interpretation of the state of the development project. Based upon this interpretation management can take action to improve the overall software quality while in the development stages.

Cited by 23

Quality Control for Global Software Development

Rick Gibson, Eugene G. McGuire · Journal of Global Information Management · 1996

Global sourcing of software has become a viable option for many U.S. corporations. Technical solutions have removed barriers such as distance and time, but quality concerns remain. Customers are demanding that their software suppliers prove they can deliver reliable, high–quality software systems on a repeatable basis. This paper examines the ISO 9000 series of standards and the process maturity guidelines offered by the Software Engineering Institute’s (SEI) Capability Maturity Model (CMM) as well as some well–known related variants of these models that offer both customers and suppliers of software guidance on improving software quality by focusing first on process.Request access from your librarian to read this article's full text.

Cited by 4

GenePRIMP: A software quality control tool

Amrita Pati · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2010

Amrita Pati of the DOE Joint Genome Institute's Genome Biology group describes the software tool GenePRIMP and how it fits into the quality control pipeline for microbial genomics. Further details regarding GenePRIMP appear in a paper published online May 2, 2010 in Nature Methods.

Cited by 0Open access

Continuous Software Quality Control in Practice

Daniela Steidl, Florian Deissenboeck, Martin Poehlmann et al. · 2014

Many companies struggle with unexpectedly high maintenance costs for their software development which are often caused by insufficient code quality. Although companies often use static analyses tools, they do not derive consequences from the metric results and, hence, the code quality does not actually improve. We provide an experience report of the quality consulting company CQSE, and show how code quality can be improved in practice: we revise our former expectations on quality control from [1] and propose an enhanced continuous quality control process which requires the combination of metrics, manual action, and a close cooperation between quality engineers, developers, and managers. We show the applicability of our approach with a case study on 41 systems of Munich RE and demonstrate its impact.

Cited by 15

Quality control software for shotgun lipidomics

Moon, HongKee, radrezza, silvia, Shevchenko, Andrej · Zenodo (CERN European Organization for Nuclear Research) · 2025

ShotGun QC software follows a modular architecture built on Observable Framework[1]’s file-based routing: each QC workflow page (src/1.Info.md through src/4.History.md and src/index.md) is authored in Markdown but orchestrates reusable visualization and analysis logic extracted into one component file (src/components/qc.js). Data dependencies are isolated (under src/data), where example peak lists, ISTD references, and loader scaffolding live, ensuring reproducible ingestion pipelines for both positive and negative ion modes. Observable CLI scripts (dev, build, deploy) provide rapid development iteration and package scripts to guarantee reproducible builds in CI(Continuous Integration): every change triggers dependency installation via Node, followed by yarn build to regenerate the release and publish it to the web. The resulting system combines deterministic QC metric computations—mass accuracy, resolution/FWHM, isotopic distribution, lipid response, ion source fragmentation, sensitivity, and MS/MS efficacy—with client-side state stored in SessionStorage to maintain context across sessions. These characteristics yield an interactive, browser-delivered QC environment that unifies automated analytics, historical tracking, and CSV export into a portable bundle (dist) suitable for deployment and visual demonstrations. [1] Observable HQ. (2024). Observable Framework. https://github.com/observablehq/framework

Cited by 0Open access

Quality control software for shotgun lipidomics

Moon, HongKee, radrezza, silvia, Shevchenko, Andrej · Zenodo (CERN European Organization for Nuclear Research) · 2025

ShotGun QC software follows a modular architecture built on Observable Framework[1]’s file-based routing: each QC workflow page (src/1.Info.md through src/4.History.md and src/index.md) is authored in Markdown but orchestrates reusable visualization and analysis logic extracted into one component file (src/components/qc.js). Data dependencies are isolated (under src/data), where example peak lists, ISTD references, and loader scaffolding live, ensuring reproducible ingestion pipelines for both positive and negative ion modes. Observable CLI scripts (dev, build, deploy) provide rapid development iteration and package scripts to guarantee reproducible builds in CI(Continuous Integration): every change triggers dependency installation via Node, followed by yarn build to regenerate the release and publish it to the web. The resulting system combines deterministic QC metric computations—mass accuracy, resolution/FWHM, isotopic distribution, lipid response, ion source fragmentation, sensitivity, and MS/MS efficacy—with client-side state stored in SessionStorage to maintain context across sessions. These characteristics yield an interactive, browser-delivered QC environment that unifies automated analytics, historical tracking, and CSV export into a portable bundle (dist) suitable for deployment and visual demonstrations. [1] Observable HQ. (2024). Observable Framework. https://github.com/observablehq/framework

Cited by 0Open access