The Science Behind Pair Programming: A UADY Experiment

\ Abstract and 1. Introduction

2. Experiment Definition

3. Experiment Design and Conduct

3.1 Latin Square Designs

3.2 Subjects, Tasks and Objects

3.3 Conduct

3.4 Measures

4. Data Analysis

4.1 Model Assumptions

4.2 Analysis of Variance (ANOVA)

4.3 Treatment Comparisons

4.4 Effect Size and Power Analysis

5. Experiment Limitations and 5.1 Threats to the Conclusion Validity

5.2 Threats to Internal Validity

5.3 Threats to Construct Validity

5.4 Threats to External Validity

6. Discussion and 6.1 Duration

6.2 Effort

7. Conclusions and Further Work, and References

Abstract

This paper presents an experience report about an experiment that evaluates duration and effort of pair and solo programming. The experiment was performed as part of a course on Design of Experiments (DOE) in Software Engineering (SE) at Autonomous University of Yucatan (UADY). A total of 21 junior student subjects enrolled in the bachelor's degree program in SE participated in the experiment. During the experiment, subjects (7 pairs and 7 solos) wrote two small programs in two sessions. Results show a significant difference (at a=0.1) in favor of pair programming regarding duration (28% decrease), and a significant difference (at a=0.1) in favor of solo programming with respect to effort (30% decrease). With only a difference of 1%, our results regarding duration and effort are practically the same as those reported by Nosek in 1998.

1. Introduction

Since the seminal work of Fisher on principles of experimental design [13], the design of experiments (DOE) for obtaining information has been widely used in natural sciences, social sciences and engineering.

\ When a researcher is designing an experiment, (s)he is interested in analyzing the effect produced in a treatment or intervention that is applied on certain objects or experimental units such as: Persons, plants, animals, etc. SE experiments use to employ persons acting as experimental units, where persons are asked to perform certain tasks that usually constitute a treatment or intervention.

\ The SE degree program at Autonomous University of Yucatan offers a course on DOE. In this course, students learn to analyze the effect produced in a treatment or intervention by using different types of experimental designs.

\ As part of this course, during the summer semester 2012 we decided to carry out an experiment; this with the aim of students learn to collect and analyze measures given an experimental design. The experiment selected for the course consisted in analyzing a couple of pair programming aspects.

\ One of the twelve main practices of extreme programming created by Kent Beck in the late 90s [3, 4] is pair programming. In this practice, two programmers work together on the same task using a computer. One of the programmers (the driver) writes the program whereas the other (the observer) reviews actively the work done by the controller. The observer reviews against possible defects, writes down annotations, or defines strategies for solving any issue that can rise over the task they are working on.

\ Some experiments have been conducted to study the effect of pair programming [24, 28, 19, 21, 22, 7, 20]. In a general way, these experiments report beneficial effects of applying this practice. Some beneficial effects reported are that it helps to produce shorter programs and helps to implement better designs; programs contain less defects than those written individually, and pairs usually require less time to complete a task than programmers working individually.

\ Under an academic context, the experiment proposed for the DOE course analyzes the duration and effort needed to write small programs in pairs and individually. The rest of the paper is organized as follows: Section 2 presents the experiment definition. Section 3 describes the design and conduction of the experiment. Section 4 presents the analysis. Section 5 discusses some experiment limitations. In section 6 we discuss the results we found. Finally, in section 7 we present the conclusions and further work.

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:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

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:::info Authors:

(1) Omar S. Gómez, full time professor of Software Engineering at Mathematics Faculty of the Autonomous University of Yucatan (UADY);

(2) José L. Batún, full time professor of Statistics at Mathematics Faculty of the Autonomous University of Yucatan (UADY);

(3) Raúl A. Aguilar, Faculty of Mathematics, Autonomous University of Yucatan Merida, Yucatan 97119, Mexico.

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