Software Quality Assurance

Buckley, Fletcher J.; Poston, Robert M.

doi:10.1109/tse.1984.5010196

Cited by 40 publications

(12 citation statements)

References 17 publications

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“…This avoided the well known hazards of a rigid and comprehensive preliminary specification, [22][23][24] reduced the risk of project failure, 25 increased the sense of ownership, and allowed close tailoring to local practice. Day to day maintenance of the system is now carried out by the information technology department of the hospital.…”

Section: Discussionmentioning

confidence: 99%

The anatomy of a clinical information system

1998

BMJ

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Section: Discussionmentioning

confidence: 99%

The anatomy of a clinical information system

1998

BMJ

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“…Traditional methods are not enough to do the necessary improvements; there need to do some changes [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Assurance for Data Analytics

Kumar¹,

Subhash²,

Fátima³

et al. 2018

ijacsa

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Abstract-Quality Assurance is a technique for ensuring the overall software quality suggested by Global Standards bodies like IEEE. The Quality Assurance for Data Analytics requires more time and a very different set of skills because Software Products, which are used for Data Analytics, are different than that of traditional ones. In result, these Software Products require more complex algorithms to operate and then for ensuring their quality, one needs more advanced techniques for handling these Software Products. According to our survey, Data Analytical Software Products require more work because of their more complex nature. One of the possible reasons can be the volume and variety of Data. On the same hand, this research emphasizes on testing of Data Analytical Software Products which have many issues because testing of these Software Products requires real data. However, every time the testing of these Software Products is based either on dummy data or simulations and these Software Products fail when they work in real time. For making these Software Products work well before and after deployment, we have to define certain Quality standards. In this way, we can get better result producing analytics Software Products for better results.

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“…We therefore advise registries aiming to prevent errors in (in-hospital) mortality registration to regularly test possible malfunctioning of software used by participants. The field of Software Engineering [21,22] provides several methods to investigate software quality by systematically testing whether pieces of computer code perform what they were specified to do. Additionally, one can monitor whether post ICU in-hospital mortality is zero for a given time period.…”

Section: Implications Of the Studymentioning

confidence: 99%

Reliability of In-hospital Mortality as a Quality Indicator in Clinical Quality Registries

Koetsier¹,

Peek²,

Jonge³

et al. 2013

Methods Inf Med

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The reliability of in-hospital mortality registration in the NICE registry was good. This was reflected by the low number of data errors and by the fact that conclusions about the quality of care were only affected for one ICU due to systematic data errors. We recommend that registries frequently verify the software used in the registration process, and compare mortality data with an external source to assure consistent quality of data.

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Software Quality Assurance

Cited by 40 publications

References 17 publications

The anatomy of a clinical information system

The anatomy of a clinical information system

Quality Assurance for Data Analytics

Reliability of In-hospital Mortality as a Quality Indicator in Clinical Quality Registries

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