Product metrics for spreadsheets—A systematic review,☆☆

https://doi.org/10.1016/j.jss.2021.110910Get rights and content
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Highlights

  • We provide the results of a literature review on spreadsheet product metrics.

  • We introduce a classification of product metrics for spreadsheets.

  • We analyze the purpose, consistency, and evaluation scenarios of product metrics.

  • We outline future directions regarding the development of spreadsheet metrics.

Abstract

Software product metrics allow practitioners to improve their products and to optimize development processes based on quantifiable characteristics of source code. To facilitate similar benefits for spreadsheet programs, researchers proposed various product metrics for spreadsheets over the last decades. However, to our knowledge, no comprehensive overview of those efforts is currently available. In this paper, we close this gap by conducting a literature review of research works that either inherently or explicitly define product metrics for spreadsheets. We scanned five major digital libraries for scientific papers that define or use spreadsheet product metrics. Based on the identified 37 papers, we created a novel catalog of product metrics for spreadsheets. The catalog can be used by practitioners and researchers as a central reference for spreadsheet product metrics. In the paper, we (i) describe the proposed metrics in detail, (ii) report how often and for what purposes the metrics are used, (iii) identify significant discrepancies in the naming and definition of the metrics, and (iv) investigate how the appropriateness of the metrics was evaluated.

Keywords

Spreadsheet quality assurance
Spreadsheet metrics
Metrics catalog
Spreadsheet product metrics
Spreadsheet metrics survey

Cited by (0)

Birgit Hofer works as researcher at Graz University of Technology. She received a Ph.D. degree in Computer Science (2013) and a Master’s degree (2009) from the same University. Her main research interests are automatic fault localization and correction in imperative and object-oriented software and spreadsheets, with a particular focus on spectrum-based fault localization, model-based debugging, and genetic programming approaches.

Dietmar Jannach is a full professor of Information Systems at the University of Klagenfurt, Austria. His main research area lies in the application of artificial intelligence technology to practical problems, with a particular focus on recommender systems in e-commerce, AI-based testing and debugging of spreadsheets, and on engineering problems of knowledge-intensive systems.

Patrick Koch obtained his Ph.D. from the University of Klagenfurt in 2019. He was a scientific project assistant at the University of Klagenfurt and afterwards at Graz University of Technology. He received his Master’s Degree in Software Development and Business Management from Graz University of Technology, Austria, in 2016. His research is focused on static analysis and AI-based techniques for debugging and quality assurance of spreadsheets.

Konstantin Schekotihin is an associate professor of Intelligent Systems at the University of Klagenfurt, Austria. His research focus lies mainly on various aspects of knowledge representation and reasoning systemsincluding machine learning, knowledge acquisition and maintenance, reasoning techniques as well as their applications in, for instance, configuration, planning, and recommendation.

Franz Wotawa received an M.Sc. and a Ph.D. from Vienna University of Technology in 1994 and 1996 respectively. He is a full professor of software engineering at Graz University of Technology. His research interests include model-based and qualitative reasoning, theorem proving, mobile robots, verification and validation, and software testing and debugging.

Authors are listed in alphabetical order.

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Editor: [Andy Zaidman].