Software Risk Assessment and Estimation Model

2008 ◽  
Author(s):  
Daya Gupta ◽  
Mohd Sadiq
Keyword(s):  
Risk Assessment ◽  
Estimation Model ◽  
Software Risk

scholarly journals Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mustafa Batar ◽  
Kökten Ulaş Birant ◽  
Ali Hakan Işık
Keyword(s):  
Risk Assessment ◽  
Software Development ◽  
Fuzzy Inference ◽  
Development Projects ◽  
Software Projects ◽  
Inference System ◽  
Rule Based ◽  
Management Method ◽  
Software Risk ◽  
Risk Assessment And Management

There is an enormous budget and financial plan in software development projects, and it is required that they take a huge investment to carry on. When looked at, the costs depend on the global substantial information about software development: in 1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only 33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way, before causing trouble that has the power of preventing successful accomplishment of software development projects, software risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried out effectively in software risk assessment and management, has been designed and developed based on the implementation of fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software development process in order to increase the success rate of the software projects. Also, the experimental results of this approach show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate and low average testing error.

scholarly journals Functional risk assessment as part of the validation in the implementation of chromatography data system

2018 ◽  
Vol 64 (01) ◽  
pp. 39-56
Author(s):  
Mena Ivanoska ◽  
Hristina Babunovska ◽  
Rumenka Petkovska
Keyword(s):  
Risk Assessment ◽  
Business Process ◽  
Laboratory Data ◽  
Data System ◽  
User Requirements ◽  
Automated Manufacturing ◽  
Validation Process ◽  
System Validation ◽  
Software Risk ◽  
Overall Risk

A Chromatography Data System (CDS) is a complex software that can be configured to the specific needs of the user’s business process. As such it falls into the Good Automated Manufacturing Practice (GAMP) 5 Category 4 – Configured Products. The validation process is planned and follows along the phases proposed by GAMP 5 for configured products. The Risk assessment stage of the CDS validation process is to carry out a risk assessment of each function of the User Requirements Specification (URS) determined on if the function is regulatory risk critical or not. The functional risk assessment is made according to the method- Failure Mode and Effects Analysis (FMEA). The Overall Risk resulting from the Risk Assessment has identified all potential failures requiring mitigating actions/controls. Mitigating actions and testing controls during the PQ phase is implemented. The final Overall Risk after implementation of Mitigating actions and testing controls during the PQ phase is not more than Medium. Keywords: chromatography data system, validation of the CDS Software, risk assessment, laboratory data integrity

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