Towards a Catalog of Spreadsheet Smells

Cunha, Jácome; Fernandes, João Paulo; Ribeiro, Hugo; Saraiva, João

doi:10.1007/978-3-642-31128-4_15

Cited by 59 publications

(55 citation statements)

References 26 publications

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“…The correctness of the output cells is determined either by the user, by comparing the results of the current spreadsheet Π with another spreadsheet considered correct, or by applying techniques to automatically detect "bad smells" [19].…”

Section: Definition 12 (The Function Cone)mentioning

confidence: 99%

On the Empirical Evaluation of Fault Localization Techniques for Spreadsheets

Hofer

Riboira

Wotawa

et al. 2013

Fundamental Approaches to Software Engineering

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Abstract. Spreadsheets are by far the most prominent example of enduser programs of ample size and substantial structural complexity. In addition, spreadsheets are usually not tested very rigorously and thus comprise faults. Locating faults is a hard task due to the size and the structure, which is usually not directly visible to the user, i.e., the functions are hidden behind the cells and only the computed values are presented. Hence, there is a strong need for debugging support. In this paper, we adapt three program-debugging approaches that have been designed for more traditional procedural or object-oriented programming languages. These techniques are Spectrum-based Fault Localization, Spectrum-Enhanced Dynamic Slicing, and Constraint-based Debugging. Beside the theoretical foundations, we present a more sophisticated empirical evaluation including a comparison of these approaches. The empirical evaluation shows that Sfl (Spectrum-based Fault Localization) and Sendys (Spectrum ENhanced Dynamic Slicing) are the most promising techniques.

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Section: Definition 12 (The Function Cone)mentioning

confidence: 99%

On the Empirical Evaluation of Fault Localization Techniques for Spreadsheets

Hofer

Riboira

Wotawa

et al. 2013

Fundamental Approaches to Software Engineering

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“…Some works aim at tackling programming errors or code smells in spreadsheets [11]. We describe in Section 4.4 the ability of our tools to detect warnings.…”

Section: Related Workmentioning

confidence: 99%

“…We describe in Section 4.4 the ability of our tools to detect warnings. General rules exposed in [11] can be implemented (at the moment we implement only a subset of the rules). Specific rules that apply to specific concepts of PCMs can also be considered.…”

Section: Related Workmentioning

confidence: 99%

Automating the formalization of product comparison matrices

Bécan

Sannier

Acher

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Product Comparison Matrices (PCMs) form a rich source of data for comparing a set of related and competing products over numerous features. Despite their apparent simplicity, PCMs contain heterogeneous, ambiguous, uncontrolled and partial information that hinders their efficient exploitations. In this paper, we formalize PCMs through modelbased automated techniques and develop additional tooling to support the edition and re-engineering of PCMs. 20 participants used our editor to evaluate the PCM metamodel and automated transformations. The results over 75 PCMs from Wikipedia show that (1) a significant proportion of the formalization of PCMs can be automated -93.11% of the 30061 cells are correctly formalized; (2) the rest of the formalization can be realized by using the editor and mapping cells to existing concepts of the metamodel. The automated approach opens avenues for engaging a community in the mining, re-engineering, edition, and exploitation of PCMs that now abound on the Internet.

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“…This paper differs from our previous work by focusing on detecting smells in spreadsheet formulas. Recently, other work on spreadsheet smells has been published [23], that aims at smells in values, such as typografical errors and values that do not follow the normal distribution.…”

Section: Related Workmentioning

confidence: 99%

Detecting code smells in spreadsheet formulas

Hermans

Pinzger

Deursen

2012

2012 28th IEEE International Conference on Software Maintenance (ICSM)

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Abstract-Spreadsheets are used extensively in business processes around the world and just like software, spreadsheets are changed throughout their lifetime causing maintainability issues. This paper adapts known code smells to spreadsheet formulas. To that end we present a list of metrics by which we can detect smelly formulas and a visualization technique to highlight these formulas in spreadsheets. We implemented the metrics and visualization technique in a prototype tool to evaluate our approach in two ways. Firstly, we analyze the Euses spreadsheet corpus, to study the occurrence of the formula smells. Secondly, we analyze ten real life spreadsheets, and interview the spreadsheet owners about the identified smells. The results of these evaluations indicate that formula smells are common and that they can reveal real errors and weaknesses in spreadsheet formulas.

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Towards a Catalog of Spreadsheet Smells

Cited by 59 publications

References 26 publications

On the Empirical Evaluation of Fault Localization Techniques for Spreadsheets

On the Empirical Evaluation of Fault Localization Techniques for Spreadsheets

Automating the formalization of product comparison matrices

Detecting code smells in spreadsheet formulas

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