| Title |
Towards early software reliability prediction for computer forensic tools (case study)
|
|---|---|
| Published in |
SpringerPlus, June 2016
|
| DOI | 10.1186/s40064-016-2539-0 |
| Pubmed ID | |
| Authors |
Manar Abu Talib |
| Abstract |
Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system. It is used, for instance, to analyze the reliability of the state machines of real time reactive systems. This research extends the architecture-based software reliability prediction model for computer forensic tools, which is based on Markov chains and COSMIC-FFP. Basically, every part of the computer forensic tool is linked to a discrete time Markov chain. If this can be done, then a probabilistic analysis by Markov chains can be performed to analyze the reliability of the components and of the whole tool. The purposes of the proposed reliability assessment method are to evaluate the tool's reliability in the early phases of its development, to improve the reliability assessment process for large computer forensic tools over time, and to compare alternative tool designs. The reliability analysis can assist designers in choosing the most reliable topology for the components, which can maximize the reliability of the tool and meet the expected reliability level specified by the end-user. The approach of assessing component-based tool reliability in the COSMIC-FFP context is illustrated with the Forensic Toolkit Imager case study. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 28% |
| Student > Bachelor | 2 | 11% |
| Student > Doctoral Student | 1 | 6% |
| Student > Ph. D. Student | 1 | 6% |
| Lecturer | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 8 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 44% |
| Engineering | 2 | 11% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Unknown | 7 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2016.
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#18,467,278
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