Relationship between Personality Traits and Software Quality

Big Five Model vs. Object-oriented Software Metrics

Anderson S. Barroso

1,2

, Jamille S. Madureira da Silva

2,3

, Thiago D. S. Souza

1,2

Bryanne S. de A. Cezario

, Michel S. Soares

and Rogerio P. C. do Nascimento

Computing Department, Tiradentes University, Aracaju, Brazil

Computing Department, Federal University of Sergipe, São Cristóvão, Brazil

Computing Department, Federal Institute of Sergipe, Aracaju, Brazil

Keywords:

Personality, Object-oriented Software Metrics, Software Development, Big Five, Software Quality.

Abstract:

The activity of analyzing personality of software developers has been a topic discussed by many researchers

over the past few years. However, their relation to software metrics has hardly been mentioned in the literature.

This work aims to identify the inﬂuence of human personality on quality of software products. At ﬁrst, a

psychological test was performed using the Big Five model for a set of developers working in industry and,

subsequently, object-oriented software metrics were applied to individual software developed by members of

the same group. As a result, it was evidenced, through statistical analysis, that the factors Consciousness,

Neuroticism and Openness to Experience have a signiﬁcant relationship with the Cyclomatic Complexity

metric. In addition, factors Extroversion, Agreeableness and Neuroticism have signiﬁcant relation with metric

Coupling between Objects. In another analysis, taking into account ideal average values for each software

metric, it was evidenced that Extroversion and Neuroticism factors have a signiﬁcant relationship with metric

Depth of Inheritance Tree. Extroversion and neuroticism were the only factors that obtained a signiﬁcant

relation with software metrics in the two proposed analyzes. Therefore, additional studies are needed to

determine any deeper connection between personality and software quality.

1 INTRODUCTION

Modern organizations are increasingly concentrating

their efforts on creativity and innovation as they strive

to remain competitive considering the pace of changes

in industry (Varona et al., 2012). Considering this

scenario, managing the software development process

has become a difﬁcult and complex task, where ac-

tivities require diverse competencies from developers.

Creativity and short-term to meet goals need to be bal-

anced throughout process.

Qualiﬁcation of software professionals is not

enough to guarantee that the project will be success-

ful to its end (Brooks Jr, 1995) (Varona et al., 2012).

This assertion comes from the fact that, every day, de-

velopers are under pressure to deliver faster results.

Thereby, external factors to their technical skills may

inﬂuence the quality of their work products.

Among several non-technical factors that may im-

pact daily work of a developer, the approach on hu-

man personality traits is the result of a historical con-

cern, given that relevant researches on the personal-

ity of software developers have been published since

the 1980’s (Bartol and Martin, 1982), (Varona et al.,

2012), (Salleh et al., 2012), (Kanij et al., 2015) (Gu-

lati et al., 2016). In most surveys, attempts are made

to identify the relationship between emotional, moti-

vational, and behavioral patterns and the software de-

velopment environment.

In this paper, we take into account two con-

cerns. First one considers software quality, because

the difﬁculties of developing, maintaining and de-

ploying software have been studied extensively over

the years. One of the main focus of studies is

software metrics for object-oriented (OO) program-

ming paradigm (Berry, 2004), (Boehm, 2006), (Wirth,

2008), (Kitchenham, 2010) and (Johari and Kaur,

2012). The second concern is with non-technical at-

tributes, more precisely the personality of the devel-

oper, since there are results that show the inﬂuence

of personality in activities of software development

(Varona et al., 2012), (Salleh et al., 2012), (Kanij

et al., 2015) and (Gulati et al., 2016).

Based on this contextualization, the objective of

Barroso, A., Silva, J., Souza, T., Cezario, B., Soares, M. and Nascimento, R.

Relationship between Personality Traits and Software Quality - Big Five Model vs. Object-oriented Software Metrics.

DOI: 10.5220/0006292800630074

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 3, pages 63-74

ISBN: 978-989-758-249-3

this work is to perform an analysis in software repos-

itories that use an object-oriented programming lan-

guage, in the software industry, to describe evidence

of developers’ personality inﬂuences on their source

code quality. Software quality was measured by ap-

plying OO metrics to software developed in C# lan-

guage (Cod, 2016), and developer personality was an-

alyzed using the Big Five model (Kanij et al., 2015).

Results were analyzed to evidence the inﬂuence of de-

veloper personality on the software developed by each

developer.

2 RELATED WORKS

Several works have considered an association be-

tween the Big Five model and Software Engineering.

The Big Five model has gained prominence in

Software Engineering research and has been applied

at both individual and team levels (Gomez and Acuna,

2007). It has been common for researchers to use the

Big Five model to analyze cooperation between soft-

ware developers and to examine pair programming

(Chao and Atli, 2006)(Hannay et al., 2010)(Salleh

et al., 2011). These studies showed a contradiction

in the inﬂuence of personality in relation to perfor-

mance. While Salleh et al. (Salleh et al., 2011) claim

that certain personality traits, such as satisfaction, sig-

niﬁcantly affect developer performance, Chao et al.

(Chao and Atli, 2006) and Hannay et al. (Hannay

et al., 2010) did not ﬁnd any statistical correlation

showing evidence of this inﬂuence.

Gulati et al. (Gulati et al., 2016) performed a study

on the relationship between personality and perfor-

mance of software engineering students and found no

positive evidence.

To the best of our knowledge, no further stud-

ies were found in the literature that correlate the Big

Five model with the application of object-oriented

software metrics to measure the quality of software.

Only one work that deals with quality of software was

found, but the authors considered group evaluation

(Gomez and Acuna, 2007), not individual developers

as performed in this paper. The authors of this article

carried out a study involving software quality (Bar-

roso et al., 2016), but with the MBTI Model (Myers

et al., 1998).

Among the related works, we identiﬁed points of

intersection between our work and previous works, as

described in the following subsections.

2.1 Empirical Study of How

Personality, Team Processes and

Task Characteristics Relate to

Satisfaction and Software Quality

In 2007, Gomez and Acuña (Gomez and Acuna,

2007) analyzed the relationships between personality,

task characteristics, product quality and satisfaction in

software development teams. Within their study, they

collected data from a sample of 35 student teams (105

participants) from a Spanish university. These teams

used eXtreme Programming (XP) to develop the same

software product. They found that the most satis-

ﬁed teams at work are precisely those whose members

scored higher for the affability and awareness factors

of the BigFive test.

Levels of satisfaction are higher when members

can decide how to develop and organize their work

and fall down when there are more conﬂicts between

team members. They also came to conclusion that ex-

troversion should be considered as a valid predictor of

software quality for software development in an ag-

ile methodology because the high interaction among

team members is essential for this method of devel-

opment. According to them, all participants could be

classiﬁed as project managers and they all were re-

sponsible for the success or failure of the product de-

veloped. In addition, they concluded that traits such

as sociability, loquacity, communicability, friendli-

ness and openness seem to be conducive to the de-

velopment of high quality software as well as to the

satisfaction of team members.

In their work (Gomez and Acuna, 2007), the au-

thors measured software quality through analysis of

source code and project documentation. The follow-

ing equation was used:

Grade = (((Modularization * 2 + Testability * 2 +

Functionality * 2 + Reusability * 2 + Style * 2) / 4) *

0.8) + ((Participation * 10 / 4) * 0.2))

For our work we take into account the statistical

analysis performed by the authors and involving the

factors of the Big Five model. In contrast we evalu-

ated quality of software in the context of information

systems developed by a single developer in an indus-

trial environment. We use object-oriented software

metrics to measure software quality.

2.2 Investigating the Effects of

Personality Traits on Pair

Programming

In 2012, Salleh et al. (Salleh et al., 2012) carried out

a set of three experiments to investigate the effect of

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

the Big Five factors in the context of students from a

university in New Zealand, in order to investigate the

inﬂuence of these factors considering programming in

pairs.

The results showed that Conscientization and

Neuroticism did not present a statistically signiﬁcant

effect on academic performance of the evaluated stu-

dents. However, Openness to Experience played a

signiﬁcant role in the academic differentiation of stu-

dents in the ﬁeld of pair programming.

The tool for performing the psychological test

used by the authors was also used in our study.

2.3 An Empirical Investigation of

Personality Traits of Software

Testers

In 2015, Kanij, Merkel and Grundy (Kanij et al.,

2015) researched whether the personality of a soft-

ware tester differs from personality of the other pro-

fessionals involved with the software development

process. They used the BigFive model and have con-

cluded, through statistical testing, that testers have

higher levels of Conscientiousness compared to other

developers. The authors themselves report that the

work has a strong threat to validity that is related

to comparison of Australian testers with testers from

other countries, since, in Australia, they used speciﬁc

test tools.

In order to mitigate this problem, our experiment

was performed in an industrial environment and used

object-oriented software metrics widely published in

the literature. These metrics measure software qual-

ity through mathematical calculations that are geo-

graphic independent.

Finally, this work has helped us to identify statisti-

cal relationships we could make between the Big Five

factors of developers and the quality of software pro-

duced by them.

3 METHODOLOGY

First we performed a review of the literature, with sys-

tematic approaches, with the purpose of identifying

the state of the art of research on the use of the Big

Five model to identify personality and its relationship

with developers in the industrial environment. Then, a

controlled experiment was conducted, which involved

the execution of a Big Five psychological test and the

extraction of OO metrics from software produced by

the participants.

According to Wohlin et al. (Wohlin et al., 2012),

an experimentation is not a simple task as it involves

preparing, conducting and analyzing experiments cor-

rectly. One of the main advantages of experimenta-

tion is the control of subjects, objects and instrumen-

tation, which makes it possible to draw more general

conclusions about the subject investigated. Other ad-

vantages include the ability to perform statistical an-

alyzes using hypothesis testing methods and opportu-

nities for replication.

Juristo et al. (Juristo and Moreno, 2013) also

claim that scientiﬁc research can not be based purely

on opinions or commercial interests. Scientiﬁc inves-

tigations are represented by studies based on observa-

tion and/or experimentation about real world and their

measurable behaviors. These aspects were observed

when preparing our experiment.

Summarizing, the experiment has 4 steps: (1) Ex-

ecution of personality test by the participants; (2) Ex-

traction of OO metrics from software produced by

participants; (3) Data collection and; (4) Analysis of

results.

4 THEORETICAL

FOUNDATIONS

Developers and project managers often devote them-

selves to the latest programming languages, frame-

works, modern processes or innovative tools, but in

all cases they are the people who design software.

Thus, the human aspects of engineering have as much

to do with the success of a project as the technical at-

tributes (Pressman and Maxim, 2014). When we talk

about human aspects in computing, we also talk about

human personality and its behavioral, emotional and

motivational characteristics.

Human personality encompasses psychological

characteristics about an individual that helps to de-

scribe differences between people, how they interact,

and adaptation to their social environment (McCrae

and John, 1998). Criteria by which people differ from

one another are called psychological traits. Traits are

representative factors for predicting one’s own pat-

terns of behavior, feeling, thinking, and related ac-

tivities (Hannay et al., 2010). From that point on, sur-

veys indicate that software developers, analysts, and

testers have different types of personalities and behav-

iors. Studies show that the skills required by a speciﬁc

developer may be related to personality style and in-

dividual behavior.

Several studies have been developed seeking to

identify personal characteristics that may inﬂuence

performance at work. These characteristics can be

Relationship between Personality Traits and Software Quality - Big Five Model vs. Object-oriented Software Metrics

identiﬁed through various personality tests. One of

the most used indicator is the Big Five test. The next

two subsections explains the Big Five model for trac-

ing personality of software developers and evaluating

software quality by means of object-oriented metrics.

4.1 Big Five Personality Model

The Big Five model was originally created in the

1970s by two independent research teams - Paul

Costa and Robert McCrae (at the National Institutes

of Health) and Warren Norman (at the University of

Michigan) / Lewis Goldberg (At the University of

Oregon) (Norman, 1967) - who have followed differ-

ent paths to achieve the same results. According to

the authors, most human personality traits can be re-

duced to ﬁve large dimensions regardless of language

or culture.

The identiﬁcation of these ﬁve dimensions was

possible after the researchers conducted interviews

with hundreds of questions to thousands of people and

then analyzed data using as factorial analysis, which

is used to reduce a large amount of information to a

synthetic and relevant set (McCrae and John, 1998)

(Norman, 1967).

In scientiﬁc circles, the Big Five is one of the most

accepted and used models to trace contemporary psy-

chological personality. This model classiﬁes human

personality into ﬁve factors:

• Extraversion: relates to the degree of sociability,

gregariousness, assertiveness, talkativeness, and

activeness (Driskell et al., 2006). A person is con-

sidered an extravert if he/she feels comfortable

in a social relationship, if he/she is friendly, as-

sertive, active, and outgoing;

• Agreeableness: refers to positive traits such as co-

operativeness, kindness, trust, and warmth. A per-

son who is low on Agreeableness tends to be skep-

tical, selﬁsh, and hostile. A team that requires a

high level of collaboration or cooperation can ben-

eﬁt from agreeable team members (Kanij et al.,

2015);

• Conscientiousness: concerned with one’s

achievement orientation. Those who have a

high score tend to be hardworking, organized,

able to complete tasks thoroughly and on time,

and reliable. On the other hand, low Conscien-

tiousness relates to negative traits such as being

irresponsible, impulsive, and disordered (Driskell

et al., 2006);

• Neuroticism: refers to the state of emotional sta-

bility. Someone low in Neuroticism tends to ap-

pear calm, conﬁdent, and secure, whereas a high

Neuroticism individual tends to be moody, anx-

ious, nervous and insecure (Driskell et al., 2006).

Neuroticism is also reported to be consistently re-

lated to self-efﬁcacy (Schmitt, 2007);

• Openness to experience: describes intellectual,

cultural, or creative interest (Driskell et al., 2006).

Someone who is high in Openness tends to ap-

pear as imaginative, broad-minded, and curious,

whereas those at the opposite end of this spec-

trum usually show a lack of aesthetic sensibilities,

preference for routine, and favouring conservative

values (Schmitt, 2007).

According to Srivastava and Kumar (Srivastava

and Kumar, 2013), the ﬁve Big Five dimensions rep-

resent one’s personality at the broadest level of ab-

straction and each dimension sums up a large number

of distinct and speciﬁc personality traits. These traits

are understood as a complete description of personal-

ity, are stable over a period of ten years and may vary

between cultures (Kanij et al., 2015).

4.2 Object-oriented Software Metrics

Software Engineering proposes several types of met-

rics that have been applied for measuring both the

process and the software product. Among those who

evaluate the product, one can cite metrics for the re-

quirements model, for the source code and also for the

project model.

For this study, object-oriented metrics were se-

lected because they are often used by researchers

in Software Engineering (Radjenovi

c et al., 2013),

for example, to reduce failures (Fenton and Bieman,

2014), in features such as maintainability, testabil-

ity and comprehensibility (Olbrich et al., 2009), for

maintenance of object-oriented software (Johari and

Kaur, 2012), and for refactoring forecast (Al Dallal,

2012).

Another positive point is that, according to a sys-

tematic review (Radjenovi

c et al., 2013), the authors

identiﬁed that object-oriented metrics were used ap-

proximately twice as much (49%) as other metrics.

As the proposed experiment was executed in an

environment that uses Microsoft C# language, the

metrics used in this work are compatible with C#

projects. Several metrics can be calculated in a project

developed in C#, using the Visual Studio platform, but

according to Microsoft itself (Cod, 2016), the follow-

ing metrics are the most important: Depth of Inheri-

tance Tree (DIT), Coupling between Objects (CBO),

Cyclomatic Complexity (CC) and Maintenance Index

(MI).

Table 1 highlights the characteristics of the ﬁve

metrics chosen for this experiment.

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Table 1: Speciﬁc Characteristic of OO Software Metrics.

Metric Characteristics

DIT (Depth of In-

heritance Tree)

Represents the number of lev-

els that a class inherits from

another class. The greater the

depth, the more complex the

project is.

CBO (Coupling be-

tween Objects)

Represents the number of

classes called by another class.

The more coupling a class has,

the more difﬁcult to understand

and maintain it is

CC (Cyclomatic

Complexity)

Represents the number of inde-

pendent paths from the source

code. The greater the complex-

ity, the more deviations in the

source code ﬂow is found (Mc-

Cabe, 1976).

MI (Maintenance

Index)

Represents the degree of main-

tainability of software based on

status of the respective source

code. (McCabe, 1976)

5 DEFINITION AND PLANNING

OF THE EXPERIMENT

In this and the next two sections, our work is pre-

sented as an experimental process following Wohlin

et al. guidelines (Wohlin et al., 2012). The focus of

this section is to describe the goal setting and experi-

ment planning.

5.1 Deﬁnition

The main elements of the experiment are the vari-

ables, the objects, the participants, the experiment

context, the hypotheses and the experimental design

type. With these elements, the objective is to carry out

studies that may evidence the improvement of some

development process. This improvement is related

to the veriﬁcation of theories formulated through hy-

pothesis of the experiment.

The objective of this work is to evaluate, through

a controlled experiment, the inﬂuence of human per-

sonality on the quality of software. The experiment

will target programmers working on a high education

institution, with at least 2 years of industry experience

and at least 1 year of programming experience using

the object-oriented paradigm.

The objective of the experiment is formalized us-

ing the GQM model originally proposed by Basili

(Basili and Weiss, 1984): Object of study: human

personality has inﬂuence on software development;

Purpose: to evaluate the Big Five model for person-

ality identiﬁcation against the application of OO soft-

ware metrics; Focus: software quality produced in-

dividually; Perspective: developers and software de-

velopment managers; Context: industry developers

working in a High Education Institution.

5.2 Planning

5.2.1 Research Hypotheses

Based on the premise that no studies associating the

Big Five personality with OO software metrics have

been found, sharing the same concern of (Gomez and

Acuna, 2007), which identiﬁes that there is a broad

ﬁeld to be worked on in the relationship between per-

sonality and quality of developed software, the re-

search items that need to be evaluated are: 1) Dif-

ferences in personality traits do not affect quality of

software developed by professionals; 2) Differences

in personality traits do affect quality of software de-

veloped by professionals.

To evaluate these items, software engineering

metrics will be used. Chosen metrics are: Depth

of Inheritance Tree (DIT), Coupling between Objects

(CBO), Cyclomatic Complexity (CC) and Mainte-

nance Index (MI) (McCabe, 1976).

With the objectives and metrics deﬁned, the fol-

lowing hypotheses are considered:

• Hypothese 1

– H0DIT: Personality traits affect software qual-

ity for DIT metric.

– H1DIT: Personality traits do not affect software

quality for DIT metric.

• Hypothese 2

– H0CBO: Personality traits affect software qual-

ity for CBO metric.

– H1CBO: Personality traits do not affect soft-

ware quality for CBO metric.

• Hypothese 3

– H0CC: Personality traits affect software quality

for CC metric.

– H1CC: Personality traits do not affect software

quality for CC metric.

• Hypothese 4

– H0MI: Personality traits affect software quality

for MI metric.

– H1MI: Personality traits do not affect software

quality for MI metric.

Relationship between Personality Traits and Software Quality - Big Five Model vs. Object-oriented Software Metrics

5.2.2 Independent Variables

Independent variables of the experiment are described

below.

• Big Five Personality Test

The goal was to conduct a psychological test with par-

ticipants (Big, 2016). The test was the same used in

the related work of Salleh et al. (Salleh et al., 2012).

Test popularly known as Big Five has 120 ques-

tions with afﬁrmations about daily activities of peo-

ple. For each statement, the experimenter must an-

swer at least one of the levels “Very Inaccurate”,

“Moderately Inaccurate”, “Neither Accurate Nor In-

accurate”, “Moderately Accurate” and “Very Accu-

rate”.

Test result indicates a score between 1 and 7 for

each factor. Each factor has a mean population score

and those who are above this score have strong char-

acteristics of the factor evaluated (see Table 2).

For adopting ideal values for the ﬁve factors that

we use in the work, we follow the values that corre-

spond to the average of the population (Big, 2016).

Values are shown in Table 2.

Table 2: Big Five ideals values.

E A C N O

H ≥ 4.44 ≥ 5.23 ≥ 5.4 ≥ 4.8 ≥ 5.38

L <4.44 <5.23 <5.4 <4.8 <5.38

E=Extraversion; A=Agreeableness; C=Conscientiousness;

N= Neuroticism; O= Openness to Experience; H=High;

L=Low

To better understand, an individual has high char-

acteristics for extraversion when he has a score

greater than or equal to 4.44 for this factor.

• Object Oriented Software Metrics

Metrics were collected using CodeAnalysis (Cod,

2016), part of Microsoft’s Visual Studio 2010, which

is the object-oriented paradigm development environ-

ment used by the chosen institution. The stored code

was on the TFS server (Team Foundation Server),

which is a Microsoft application life cycle manage-

ment collaboration platform (TFS, 2016).

The chosen institution for experiment was iden-

tiﬁed as having all the prerequisites required to per-

form the tests: it had a well-deﬁned development

framework, using Microsoft Visual Studio, it had soft-

ware developed by a single developer with controlled

changes. An important point is that the target institu-

tion does not use tools that generate automatic source

code, which could interfere in the results of the indi-

vidual quality of the developers (Gomez and Acuna,

2007).

For each software produced by the participants,

the OO software metrics disposed in Table 3 were ap-

plied.

For adopting ideal values for the ﬁve metrics to

be use at work, developers followed McCabe’s guide-

lines (McCabe, 1976) and Bhasin, Sharma and Popli

(Bhasin et al., 2014), which deﬁne a set of “good”,

“regular” and “critical” values for each metric evalu-

ated, as presented in Table 3.

Table 3: C# metrics ideal values.

DIT CBO CC MI

Good 1-2 0-9 1-10 20-100

Regular 3-4 10-30 11-20 10-19

Critical > 4 > 30 >20 < 9

5.2.3 Dependent Variables

Dependent addressed variables were: Average of

Maintainability Index (MI); Average of Cyclomatic

Complexity (CC); Average of Depth of Inheritance

(DI); Average of Coupling between Objects (CBO).

5.2.4 Intervener Variables

We can highlight two variables that can inﬂuence re-

sults of the experiment:

• Developers’ experience in the applied psycholog-

ical test, though all have conﬁrmed that they had

never tested;

• Developers’ commitment to respond the test.

5.2.5 Participants Selection

Participants are the individuals selected to conduct the

experiment. They are responsible for informing pa-

rameters for the experiment, such as the value of vari-

ables.

For our experiment, we took into account software

implemented by a single developer, considering that

analysis “personality X software quality” should be

1x1.

In this context, the versioning repository of the in-

stitution, by means of TFS (TFS, 2016), is used to

verify which developers ﬁt the pre-established condi-

tions. Twenty softwares were found that were devel-

oped by a single developer.

According to the software manager, we found out

that among the twenty developers, ﬁfteen were still

working in the company. In order not to violate the

principle of randomness and to avoid interference in

the outcome of the experiment, all 15 participants per-

formed the psychological test at one time (Shull et al.,

2001).

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Chosen institution was interested in ascertaining

the inﬂuence of developers’ personality on quality of

software, cooperating with the researchers and au-

thors of this article, who are totally impartial, without

having any demand, interest or preference.

5.2.6 Experiment Project

Experiment was designed so that all participants per-

formed the psychological test Big Five (Big, 2016).

All 15 participants performed the experiment at one

time to mitigate exchange of experiences between

them.

Experimenters explained about psychological Big

Five test and told the participants that they could ask

for help for understanding the issues. Participants

were placed in a computer lab at the institution se-

lected. They did not talk to each other during the test

period.

After the test was executed, the experimenters in-

formed the participants that the Big Five test result

would be associated with quality of software they had

developed. Experimenters also reported that they had

already collected the values of the software metrics.

Participants were told that the results would be dis-

closed to the IT manager.

5.2.7 Instrumentation

Instrumentation process was initially performed with

conﬁguration of environment for the experiment and

planning of data collection. This was performed in the

computer lab of the selected Institution. The test was

performed with people in their working environment,

setting up an experiment in vivo.

The tools used were: Code Analisys, part of Vi-

sual Studio 2010, version 10.0.40219.1, coupled to

the Microsoft .NET Framework, version 4.6.01055

(Cod, 2016); Team Foundation Server with last up-

date on 26/09/2016 (TFS, 2016); Online tool for run-

ning the Big Five test (Big, 2016).

6 EXPERIMENT OPERATION

In the following sections we will report on how the

experiment process occurred, highlighting data prepa-

ration, execution and validation.

6.1 Experiment Preparation

The following steps were considered: Characteri-

zation Form- participants answered questions about

formal training and work experience; Software

Metrics Collection- experimenters collected object-

oriented metrics from software developed by partic-

ipants; Allocation of developers to respond to Big

Five psychological test - the computers were already

previously connected with link to run the test. Partic-

ipants did not have to make any conﬁguration; Feed-

back Form- Participants answered questions about

the experience with the experiment, whether liked the

test and whether think the test was effective in tracing

the personality.

6.2 Experiment Execution

After preparation of the experiment and initial in-

structions, participants had had ﬁve minutes to set-

tle in. Each participant had ten minutes to answer

the characterization form. At this point, there was no

doubt as to the interpretation of what was being asked.

All participants agreed that associating personal-

ity with software quality could be a positive point in

the individual assessment of the developer. However,

the participants were not informed about the exact hy-

potheses to be tested, avoiding the phenomenon De-

mand Characterization (Orne, 1962).

Data Collect

Experimenters collected metrics from software devel-

oped individually by each participant. The experi-

menter copied the screen with each participant’s Big

Five test result. Upon completion of the test, the de-

veloper called one of the experimenters to copy the

test response to a ﬂash drive, identifying the ﬁle with

the participant’s name.

Results of the collected data are presented in the

Results Analysis Section.

6.3 Data Validation

As support for analysis, interpretation and validation,

two types of statistical tests were applied:

• Shapiro-Wilk test to verify the normality of the

samples, since it is an indicated test for sam-

ples smaller than 30 (Shapiro and Wilk, 1965)

(Boslaugh, 2012).

• Mann-Whitney (U Test) test to verify the level of

relationship between Big Five psychological fac-

tors and OO Software Metrics, since we are deal-

ing with two independent samples. This test is

commonly applied for the analysis of psycholog-

ical factors (Mann and Whitney, 1947)(MacFar-

land and Yates, 2016). Another justiﬁcation is that

the related work of (Kanij et al., 2015) obtained

satisfactory results using this test.

Relationship between Personality Traits and Software Quality - Big Five Model vs. Object-oriented Software Metrics

The statistical tests were performed using the R

tool, created by the Foundation for Statistical

Computing, version 3.2.2.

7 ANALYSIS OF RESULTS

In this section we will continue with the Analysis and

Results Interpretation and with the Threats to the Va-

lidity of the Experiment.

7.1 Analysis and Results Interpretation

In this section we will discuss the results of the exper-

iment.

7.1.1 Participants Analysis

All participants had proven experience in software de-

velopment and were appropriately tailored to the C#

development model used by the institution.

Although the level of experience and the number

of software maintained were different among partic-

ipants, no speciﬁc training was required for develop-

ment, since the metrics were taken from software that

they had already developed and were already in use

by the users of the institution.

7.1.2 Results of Big Five Test

Figure 1 depicts the distribution of factors by each de-

veloper who performed the test. To better explain the

distribution, note that the participant P1 scored 4 for

the Extraversion factor, 5.5 for Agreeableness, 5.5 for

Consciousness, 6 for Neuroticism, and 5 for Open-

ness to Experience.

Comparing the participants, we can point out that

participants P8, P10 and P15 are the least extroverted

and participants P2, P7 and P13 are the most extro-

verted. Participants P2 and P9 obtained the lowest

scores for the Neuroticism factor.

Factor Openness to Experience was the one that

obtained less variation between the scores obtained

by participants.

7.1.3 Software Metrics Values

Figures 2, 3, 4 and 5 represent the distribution of OO

software metrics for softwares developed individually

by each participant.

Taking into account Table 3, which describes the

quality levels acceptable in the literature for each met-

ric, we can point out that all 15 participants main-

tained levels considered “good” for the Maintenance

Index (Fig. 2), Cyclomatic complexity (Fig. 4) and

Coupling between objects (Fig. 5).

Participants P3, P9, and P15 developed software

with a level considered “good” for metric Depth of

Inheritance Tree (Fig. 3). In contrast, participants P4,

P5, P7 and P8 are rated at a “critical” level consider-

ing the same metric.

7.1.4 Relationship between Big Five and OO

Metrics

For analysis of results, we used conclusive statistical

evidence. First, a signiﬁcance level of 0.05 was set

for the whole experiment, as well as the Shapiro-Wilk

test (Boslaugh, 2012) was applied to verify if the sam-

ples have a normal distribution. Data are presented in

Table 4.

Table 4: Shapiro-Wilk normality test.

W p-value

Extroversion 0.9522 0.561

Agreeableness 0.9362 0.3379

Conscientiousness 0.8989 0.0916

Neuroticism 0.9516 0.5506

Openness to Exp. 0.9006 0.0973

Maintainability Index 0.9713 0.8772

Cyclomatic Complexity 0.9860 0.9951

Depth of Inheritance Tree 0.8940 0.0773

Coupling Between Objects 0.9719 0.8863

By respecting the level of signiﬁcance of 0.05

adopted, we can observe that all samples have normal

distribution, that is, they have p-values higher than

0.05. Results of normality test indicate that our sam-

ple is normally distributed for each of the Big Five

factors and for each of the OO software metrics ana-

lyzed.

From this point, we will analyze two different sce-

narios. In the ﬁrst one, we will make the general rela-

tion of psychological factors with OO software met-

rics. In the second, we will make the relation tak-

ing into account the ideal values found in Tables 2

and 3. For both scenarios, we will adopt a signiﬁ-

cance level of 0.05 and apply the Mann-Whitney test

(U Test) to provide evidence of relationship between

variables. In the ﬁrst scenario, we provide relation

of each factor individually with all collected software

metrics, each value of Table 5 represents the level of

signiﬁcance (p-value) of the relationship between the

psychological factor and the analyzed OO software

metrics. These two scenarios will help comparing the

results of our research with the results of related work.

In order to ﬁnd evidence of the relation, p-value

must be greater than the signiﬁcance level 0.05. By

observing Table 5, one can observe that the level of

signiﬁcance is reached in the following relationships:

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Figure 1: Participants X Factors Big Five.

Figure 2: Participants X MI Metric.

Figure 3: Participants X DIT Metric.

E x CBO; A x CBO, C x CC, C x CBO, N x CC, N x

CBO and O x CC.

In this ﬁrst scenario, by adopting a general con-

text, we can show that only the CC and CBO met-

rics are inﬂuenced by some Big Five psychological

factors. Therefore, we can refute the null hypotheses

Figure 4: Participants X CC Metric

Figure 5: Participants X CBO Metric.

H0MI and H0DIT and accept the alternative hypothe-

ses H1CBO and H1CC.

In the second scenario, we take into account the

level of scores of the psychological factors (Table 2)

and the ideal levels of OO metrics. We perform an

analysis in Figs. 2, 3, 4 and 5, and observe that met-

Relationship between Personality Traits and Software Quality - Big Five Model vs. Object-oriented Software Metrics

Table 5: Scenario 1: Relation Big Five factors X Metrics

OO.

MI CC DIT CBO

E 0,0000610 0,0114 0,0215 0,2454

A 0,000724 1 0,00262 0,0596

C 0,000061 0,550 0,00132 0,1026

N 0,0000610 0,0648 0,02155 0,6701

O 0,00072 0,599 0,000061 0,0445

E=Extraversion; A=Agreeableness; C=Conscientiousness;

N= Neuroticism; O= Openness to Experience

rics MI, CC and CBO had values considered homo-

geneous and classiﬁed as “good” according to Table

3. Only the DIT metric has heterogeneous values for

the acceptable levels. Based on this analysis, we used

only the DIT metric in the assessment of scenario 2.

Once again we adopted a signiﬁcance level of 0.05

and the Mann-Whitney test (U Test).

Only the Extraversion and Neuroticism factors

obtained levels of signiﬁcance higher than 0.05 (p-

value), compared to the DIT software metric.

Thus, after analyzing scenario 2 separately, there

is evidence that respecting the ideal values for psy-

chological factors and OO metrics, we can refute the

H0DIT hypothesis and accept the alternative hypoth-

esis H1DIT.

In summary, the analysis of results of the pro-

posed experiment, in a ﬁrst scenario, without taking

into account the ideal levels of psychological factors

and OO metrics, evidenced the inﬂuence of some Big

Five factors over the metrics Cyclomatic Complexity

(CC) and Coupling between Objects (CBO).

In a second scenario, by considering ideal levels

as presented in Tables 3 and 2, the analysis evidenced

inﬂuence of the factors Extraversion and Neuroticism

on the metric Depth of Inheritance Tree (DIT). A

comparison between the two scenarios is depicted in

Table 6.

Table 6: Comparison between the two scenarios analysed.

Scenario 1 Scenario 2

MI CC DIT CBO DIT

E X X

A X

C X X

N X X X

O X

E=Extraversion; A=Agreeableness; C=Conscientiousness;

N= Neuroticism; O= Openness to Experience

Extroversion and neuroticism were the only fac-

tors that obtained a signiﬁcant relation with software

metrics in the two analyzes proposed.

7.1.5 Comparison with Related Work

In order to create a comparison with related works,

we will analyze Table 7.

Table 7: Comparison between related works and the two

scenarios.

Factor

Our work

T1 T2 T3

Scenario 1 Scenario 2

E X X

A X X

C X X

N X X

O X X X

E=Extraversion; A=Agreeableness; C=Conscientiousness;

N= Neuroticism; O= Openness to Experience

As discussed in Section on related works, authors

Gomez and Acunã (Gomez and Acuna, 2007), in

work T1, found that the factors Agreeableness and

Openness to Experience may be conducive to the de-

velopment of software with high quality, remember-

ing that they analyzed the project and not each metric

individually. In our study, these two factors also had

signiﬁcant values. Agreeableness obtained signiﬁcant

relation in scenario 2 and Openness to Experience in

scenario 1.

Authors of work presented in (Salleh et al., 2012)

(work T2), indicated that the Openness to Experience

factor can play a signiﬁcant role in the academic dif-

ferentiation of students in the ﬁeld of pair program-

ming. In our work, we also found a signiﬁcant rela-

tionship for Opening the Experience in scenario 1.

Finally, authors of work T3 (Kanij et al., 2015)

have reached the conclusion that software testers have

higher levels of Conscientiousness compared to other

development professionals. In our work, Conscien-

tiousness also obtained a level of signiﬁcance relevant

to scenario 1 of the experiment.

After this analysis, we can observe that our ﬁnd-

ings are consistent with those found in the literature,

and that there is evidence of the inﬂuence of person-

alities in the quality of software in the experimented

environment.

7.2 Threats to Validity

Even though we have achieved satisfactory results in

the experiment, we can not disregard the following

threats to validity.

• Internal validity: Since the applied Big Five test

had 120 questions, it is possible that the devel-

oper lost concentration at some point of the test.

This was mitigated with clariﬁcation from experi-

menters as to the importance of focusing on each

issue.

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

• External validity: Since in the experiment envi-

ronment there were only 15 developers who sat-

isﬁed the main prerequisite of the experiment (to

have developed software or part of it alone, with-

out any other developer having helped or par-

ticipated in the project), the results could have

changed if the number of participants was greater.

The difference in complexity between the soft-

wares was mitigated by the use of the values mean

of metrics. According to work (Kanij et al., 2015)

the BigFive factor characteristics are understood

as a complete description of personality and are

stable over a period of ten years. With this there

was the concern in choosing software under 10

years.

• Construct validity: The website (Big, 2016) that

had the Big Five test applied may not have been

intuitive enough. This was mitigated with a thor-

ough explanation on the site and the test ques-

tions.

8 CONCLUSION AND FUTURE

WORKS

The objective of experiment presented in this paper

was to verify if there is a relation between the psy-

chological factors of a developer and the quality of the

software they produce. The experimenters conducted

a controlled experiment with 15 software developers

working in an educational institution. This institution

had all the necessary prerequisites for the experiment,

considering that the company owns software, or parts

of it, developed by a single developer.

In the experiment, the experimenters collected

data from Big Five psychological test of the 15 devel-

opers and collected the software metrics developed by

the same 15 developers.

Through an analysis of results for collected sam-

ples, we found evidence that when we relate the Big

Five factors to the software metrics, without concern

for the ideal levels adopted in the literature, (see Ta-

bles 2 and 3), one can suggest that CC metric was

inﬂuenced by the factors Consciousness, Neuroticism

and Openness to Experience, and the CBO metric was

inﬂuenced by the Extraversion, Agreeableness and

Neuroticism factors. On the other hand, if there is

concern about the ideal levels adopted in the litera-

ture, it is possible to suggest that the DIT metric was

inﬂuenced by the Extraversion and Neuroticism fac-

tors.

One important ﬁnding is that Neuroticism factor

was the only one to generate positive evidence in

the two evaluated scenarios. That is, the higher the

Neuroticism score, the greater the chances the devel-

oper will control Anxiety, self-awareness and anger.

He/she will hardly have sign of depression and will be

less vulnerable to sudden mood swings. These quali-

ties can help the developer to improve quality of their

software, at least with respect to the Cyclomatic Com-

plexity (CC), Coupling Between Object (CBO) and

Depth of Inheritance Tree(DIT) metrics.

Further studies are needed to determine any

deeper connection between personality and software

quality. As for future work, we suggest that the same

experiment can be performed with a larger number of

participants and in a different development environ-

ment, since the experiment was executed in a single

geographical location.

ACKNOWLEDGEMENTS

The authors would like to thank the Brazilian re-

search agency CNPq (grant 445500/2014-0) for ﬁnan-

cial support.

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