• Corpus ID: 13401801

Spreadsheet Auditing Software

@article{Nixon2010SpreadsheetAS,
  title={Spreadsheet Auditing Software},
  author={D. Nixon and Mike O'Hara},
  journal={ArXiv},
  year={2010},
  volume={abs/1001.4293}
}
It is now widely accepted that errors in spreadsheets are both common and potentially dangerous. Further research has taken place to investigate how frequently these errors occur, what impact they have, how the risk of spreadsheet errors can be reduced by following spreadsheet design guidelines and methodologies, and how effective auditing of a spreadsheet is in the detection of these errors. However, little research exists to establish the usefulness of software tools in the auditing of… 
The Detection of Human Spreadsheet Errors by Humans versus Inspection (Auditing) Software
TLDR
This study attempted to find errors in human-developed spreadsheets to avoid the potential artifacts created by error seeding, and found Excel Error Check and Spreadsheet Professional were almost useless for correctly flagging natural (human) errors.
Evaluating the Effectiveness of Static Analysis Programs Versus Manual Inspection in the Detection of Natural Spreadsheet Errors
TLDR
The results showed that while manual human inspection results for this study were consistent with previous research in the field, the performance of the static analysis programs at detecting natural errors was very poor for every category of spreadsheet errors.
Copy-Paste Detection in Spreadsheets
TLDR
An algorithm to detect data clones within spreadsheets: formulas whose values are copied in a different location and it is shown that this algorithm is able to detect these data clones with precision rates similar to those achieved by state-of-the-art code clone detection algorithm.
Complexity Metrics for Spreadsheet Models
TLDR
Two conceptual constructs - the reference branching condition cell and the condition block - are discussed, aiming at improving the reliability, modifiability, auditability and comprehensibility of logical tests.
WARDER: Refining Cell Clustering for Effective Spreadsheet Defect Detection via Validity Properties
TLDR
This paper discusses and improves one state-of-the-art technique, CUSTODES, which uses cell clustering and anomaly detection to extend its scope and make its patterns adaptive to varying spreadsheet styles, but is prone to fragile clustering when involving irrelevant cells, leading to a largely reduced detection precision.
Reducing Error in Spreadsheets: Example Driven Modeling Versus Traditional Programming
TLDR
Experimental data supporting an alternative approach to developing decision support spreadsheets using a Programming by Demonstration paradigm is presented and benefits and limitations this method offers are described through statistical analysis of the experimental results.
XLSearch: A Search Engine for Spreadsheets
TLDR
The XLSearch system is presented, a novel search engine for spreadsheets that indexes spreadsheet formulae and efficiently answers formula queries via unification (a complex query language that allows metavariables in both the query as well as the index).
CUSTODES: Automatic Spreadsheet Cell Clustering and Smell Detection Using Strong and Weak Features
TLDR
CUSTODES is proposed to effectively cluster spreadsheet cells and detect smells in these clusters using strong and weak features and successfully detected harmful smells that can induce computation anomalies in spreadsheets with an F-measure of 0.72, outperforming state-of-the-art techniques.
A Review of Spreadsheet Error Reduction Techniques
TLDR
This paper presents a meta-analyses of the EMMARM, a large-scale probabilistic study of the determinants of infectious disease in eight operation rooms of the immune system and its consequences.
...
...

References

SHOWING 1-9 OF 9 REFERENCES
Risk Assessment For Spreadsheet Developments: Choosing Which Models to Audit
TLDR
Risk assessment based on the "SpACE" audit methodology used by H M Customs & Excise's tax inspectors is described, which allows the auditor to target resources on the spreadsheets posing the highest risk of error, and justify the deployment of those resources to managers and clients.
Classification of Spreadsheet Errors
TLDR
A framework for a systematic classification of spreadsheet errors is described, aimed at facilitating analysis and comprehension of the different types of spreadsheeterrors.
Spreadsheet Errors: What We Know. What We Think We Can Do
TLDR
To date, only one technique, cell-by-cell code inspection, has been demonstrated to be effective, and the degree to which other techniques can reduce spreadsheet errors needs to be determined.
A Structured Methodology for Spreadsheet Modelling
TLDR
A methodology for structured software development is proposed, which is based on structured analysis of data, represented as Jackson diagrams, and it is shown that this analysis allows a straightforward modularisation, and that individual modules may be represented with indentation in the block-structured form of structured programs.
Spreadsheet Engineering: A Research Framework
TLDR
Spreadsheet engineering adapts the lessons of software engineering to spreadsheets, providing eight principles as a framework for organizing spreadsheet programming recommendations to overcome the heterogeneity of spreadsheet users.
Visual Checking of Spreadsheets
TLDR
New visual methods of showing the deep structures of a spreadsheet and how these visual methods can be employed in various interactive local and global debugging strategies are presented.
New Guidelines For Spreadsheets
TLDR
Rules of style for text, graphics, and mathematics are examined and a new style is applied to spreadsheets, which contrasts with the existing programming style.
2000),”Visual Checking of Spreadsheets
  • Proc. EuSpRIG,
  • 2000
Spreadsheet Errors: What We Know
  • What We Think We Can Do”, Proc. EuSpRIG,
  • 2000