reproducer (version 0.4.0)

KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC: KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC data illustrate correlations between results from individual participants in a family of 2 cross-over experiments conducted by Gravino et al.: [1] C. Gravino, G. Scanniello, and G. Tortora, "Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication," Journal of Visual Languages and Computing, vol. 28, pp. 23<U+2013>38, 2015. The experiments assess whether the comprehension of object oriented source-code increases used with UML class and sequence diagrams produced in the software design phase. If you use this data set please cite: [1] C. Gravino, G. Scanniello, and G. Tortora, "Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication," Journal of Visual Languages and Computing, vol. 28, pp. 23<U+2013>38, 2015. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

Description

KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC data illustrate correlations between results from individual participants in a family of 2 cross-over experiments conducted by Gravino et al.: [1] C. Gravino, G. Scanniello, and G. Tortora, "Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication," Journal of Visual Languages and Computing, vol. 28, pp. 23<U+2013>38, 2015. The experiments assess whether the comprehension of object oriented source-code increases used with UML class and sequence diagrams produced in the software design phase. If you use this data set please cite: [1] C. Gravino, G. Scanniello, and G. Tortora, "Source-code comprehension tasks supported by UML design models: Results from a controlled experiment and a differentiated replication," Journal of Visual Languages and Computing, vol. 28, pp. 23<U+2013>38, 2015. [2] Barbara Kitchenham, Lech Madeyski, Giuseppe Scanniello and Carmine Gravino, "The importance of the Correlation between Results from Individual Participants in Crossover Experiments" (to be submitted as of 2020).

Usage

KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC

Format

A data frame with 64 rows and 9 variables:

ExperimentID

<fct>|ExperimentID: A unique identifier for each of the three experiments in the data set.

ParticipantID

<fct>|Participant ID: An identifier for each participant, unique for a specific experiment.

SequenceGroup

<fct>|Experimental Sequence Group: A , B , C, D

System

<fct>|Software systems used in the experiment: Music shop, a system for handling the sales of a music shop. Theater ticket, a system for managing theatre reservations.

Period

<fct>|Time period of the cross-over experiment: 1 or 2

Treatment

<fct>|Experimental Treatment: Mo, design models were available, NOMo design models were not available

Comprehension

<dbl>|Dependent variable: The level of comprehension achieved by the software engineer.

Time

<dbl>|Dependent variable: The time [min] taken to complete the comprehension task.

CrossOverID

<fct>|CrossOver category: For 4 group crossover designs, the crossover category specifies the matching pairs of sequence groups, CO1 and CO2. For 2 group crossover, the category is set to CO1 only

Examples

# NOT RUN {
KitchenhamEtAl.CorrelationsAmongParticipants.Gravino15JVLC
# }