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SoTesTeR: Software Testing Techniques’ Recommender System Using a Collaborative Approach

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Information Management and Big Data (SIMBig 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 898))

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Abstract

Software testing is a key factor on any software project; testing costs are significant in relation to development costs. Therefore, it is essential to select the most suitable testing techniques for a given project to find defects at the lower cost possible in the different testing levels. However, in several projects, testing practitioners do not have a deep understanding of the full array of techniques available, and they adopt the same techniques that were used in prior projects or any available technique without taking into consideration the attributes of each testing technique. Currently, there are researches oriented to support selection of software testing techniques; nevertheless, they are based on static catalogues, whose adaptation to any niche software application may be slow and expensive. In this work, we introduce a content-based recommender system that offer a ranking of software testing techniques based on a target project characterization and evaluation of testing techniques in similar projects. The repository of projects and techniques was completed through the collaborative effort of a community of practitioners. It has been found that the difference between recommendations of SoTesTeR and recommendations of a human expert are similar to the difference between recommendations of two different human experts.

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Author information

Authors and Affiliations

  1. Universidad Nacional Mayor de San Marcos, Lima, Peru

    Ronald Ibarra & Glen Rodriguez

  2. Universidad Nacional Agraria de la Selva, Tingo María, Peru

    Ronald Ibarra

Authors
  1. Ronald Ibarra
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  2. Glen Rodriguez
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Corresponding author

Correspondence to Glen Rodriguez .

Editor information

Editors and Affiliations

  1. Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Juan Antonio Lossio-Ventura

  2. Fondazione Bruno Kessler, Trento, Italy

    Denisse Muñante

  3. Facultad de Ingeniería, University of the Pacific, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

A Recommendations for Target Projects (TP)

A Recommendations for Target Projects (TP)

TP

Recommender

Recommendation

TP

Recommender

Recommendation

0

E1

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6

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FGN, NL, FT

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E2

DCC, DT, DCH

6

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OA, FT, DTC

0

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DT, STD, FI

6

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CL, BC, FT

0

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BC, DCC, CW

6

RS

DCC, GM, OA

1

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DT, STD, APT

7

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DT, CW, SC

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7

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Legend

.DT: Decision Tables

OA: Orthogonal Arrays

ECP: Equivalence Class Partitioning

FC: Function Coverage

FI: Formal Inspections

BVA: Boundary Value Analysis

DCC: Decision/Condition Coverage

GM: Graph Matrices

DCH: Desk checking

FGN: Flow Graph Notation

STD: State Transition Diagrams

FT: Fuzz Testing

BC: Branch coverage

SL: Simple Loops

CW: Code walkthrough

CL: Concatenated loops

APT: All Pairs Technique

SC: Statement coverage

DTC: Deriving Test Cases

NL: Nested loops

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Ibarra, R., Rodriguez, G. (2019). SoTesTeR: Software Testing Techniques’ Recommender System Using a Collaborative Approach. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_28

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  • DOI: https://doi.org/10.1007/978-3-030-11680-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11679-8

  • Online ISBN: 978-3-030-11680-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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