A Field Research on the Practices of High Performance Software

Engineering Teams

Alessandra C. S. Dutra

, Rafael Prikladnicki

and Tayana Conte

School of Technology, Pontifícia Universidade Católica do RS (PUCRS), Porto Alegre, Brazil

Department of Computing, Universidade Federal do Amazonas (UFAM), Amazonas, Brazil

Keywords: Software Engineering Education and Training, High Performance Teams, Field Research.

Abstract: This paper presents the results of a field research aiming at identifying the practices adopted by High

Productivity Software Engineering Teams .This field research was developed through interviews with project

managers from several companies with the following objectives: to evaluate the knowledge of the

professionals in relation to the characteristics of the high performance teams found in the literature; understand

and identify which practices companies use to develop each high performance characteristic; identify the

training approaches that are used to improve the professionals in each practice.

1 INTRODUCTION

The software development market operates in a

global environment, with rapid changes, and needs to

respond to these new opportunities and new markets

with agility (Sommerville, 2010). Achieving agility,

competitiveness and results without a qualified

software development team and high performance is

a difficult task and can bring results that are not very

competitive.

A study done in 2015 by Standish Group (Hastie

and Wojewoda, 2015) with a sample of 10,000

projects around the world produced a report called

“Chaos Manifesto 2016”, which revealed that the

Information Technology (IT) industry faces several

challenges; although 29% of the IT projects have

been successful, being delivered before the deadline

and within the estimated cost; 52% of the IT projects

were delivered after the deadline and more expensive

than the original plan; and 19% of the IT projects

were total failures, being cancelled before the

delivered time, or were delivered but never used.

Faraj and Sambamurthy (2006) say that improving

the productivity and quality of projects are important.

Initial approaches were focused on discovering better

methodologies and tools, but there is an increasing

perception that the projects also face several challenges

related to communication, coordination, learning,

negotiation, diversity and on how to form high

performance teams for software development projects.

This context indicates that the qualified education

and training of professionals is more necessary in the

society in which we live. Whether in short courses, or

at the undergraduate or graduate level, training good

professionals it is part of the commitment a Higher

Education Institution (HEI) has in the society

(Dannelly and Steidley, 2001). Beckman (Beckman

et al.,1997) say that, among other factors, the quality

of the professional is directly related to the quality of

the education he/she received.

The quality of SE training can contribute

meaningfully to improvements in the state of the art

of software development and aid in solving some

traditional problems and crises related to software

industry practices (Gibbs, 1994). Training and

capacity-building to prepare a software professional

must include not only basic knowledge of the

Computer Science field, but also the teaching of

concepts, processes and techniques for the definition,

development and maintenance of

software (Saiedian,

1999; ACM/IEEE, 2008).

As a result, the education process in Software

Development has begun to question the methods used

in training activities (Beckman et al., 1997). Recent

studies observe that these methods involve traditional

teaching strategies such as theory presentation,

expositive classes and complementary reading. In this

scenario, students find in the industry a different

scenario than what is taught in academia (Prikladnicki

et al., 2009). At the same time, software development

projects have required high performance team

Dutra, A., Prikladnicki, R. and Conte, T.

A Field Research on the Practices of High Performance Software Engineer ing Teams.

DOI: 10.5220/0007722502450252

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 245-252

ISBN: 978-989-758-372-8

245

training, and professionals with strong technical,

behavioural, and business skills which current

educational programs are not able to supply

(Monsalve et al., 2011). One of the reasons is the fact

that such programs concentrate on basic education

focused on the traditional approaches for software

development, instead of preparing the professional to

act as a part of a software development team, which

requires multifunctional competencies and a

multidisciplinary environment.

Thus, the goal of this paper is to develop a

reflection about how the current existing SE training

approaches cover the various high performance teams

characteristics. We first conducted an ad-hoc

literature study about the existing training approaches

in SE and then a systematic literature review (SLR)

about high performance teams characteristics. At the

end, we reflected on how the existing training

approaches help in forming high performance

software development teams.

This paper is divided into six sections. In Section

2 we present the theoretical foundations. In Section 3,

we report on existing training approaches. Section 4

provides a field research on high performance teams

practices. Finally, in Section 5 the conclusions and

future work are addressed.

2 BACKGROUND

2.1 Software Engineering Training

Software Engineering is concerned with theory

application, knowledge and practice for the effective

and efficient software development of systems that

satisfies users requirements (ACM/IEEE, 2008). SE

began to be discussed as a discipline in 1968

(ACM/IEEE, 2004) and currently is part of the

curriculum of several courses such as Computer

Science, Computer Engineering, Information

Systems, Automation Control Engineering and

Software Engineering.

Software Engineering is related with all software

production aspects, from the initial stage to its

maintenance, involving not only technical

development processes, but also project management

activities and tools, methods and theories that support

its production (Sommerville, 2010). Therefore, SE

goes beyond programming code creation; it tries to

discipline development and brings to software

development principles, techniques and knowledge to

discuss quality questions, deadlines and economic

factors (ACM/IEEE, 2004).

The professionals who conclude their

undergraduate course, according to curricular

recommendations, are able to, among other aspects,

master knowledge and abilities that are part of the SE

area; work individually or as part of a team to develop

software artefacts with quality; design solutions using

appropriate SE approaches that integrate ethical,

social, legal and economic questions; know how to

apply current theories, models and techniques that

provide a baseline for identifying and analyzing

problems, software design, development, implement-

tation, verification and documentation; demonstrate

understanding and appreciation of the importance of

negotiation, efficient work habits, leadership, and

good communication with stakeholders; and learn

new models, techniques and technologies as soon as

them emerge (ACM/IEEE, 2004).

By analyzing the curricular recommendation

listed, we have identified that there are several

required competencies for a SE professional. The SE

curriculum (ACM/IEEE, 2004), (ACM/IEEE, 2008)

points to the necessity of education apart from

expositive class formats, and one of the way to

increase education quality involves innovative

strategies and didactics. According to Beckman

(Beckman et al., 1997), educational quality is one of

the important factors that influence the quality of the

professionals. Thus, some of the challenges for

improving SE education are: to make SE courses

more attractive to students; to focus appropriately on

SE education, understanding its dimensions; to

present industry practices to the students; provide

education to industry professionals; to make

education in SE evidence-based; to ensure that SE

educators have the necessary experience and

knowledge to this assignment; and to increase the

research prestige and quality of the educational SE

(Sommerville, 2010).

According to Conn (Conn, 2002), the SE

professionals are dissatisfied with the lack of training

of the university students that enter the work market,

which means that the industry must complement their

education with training that gives them necessary

knowledge in order to make up this deficiency. This

training can involve professionals or teams, including

high performance teams.

2.2 High Performance Teams

A high performance team is a group that brings

together members committed to the mutual growth

and personal success. According to Chiavenato

(2008), the main high performance teams attributes

are: participation, accountability, clarity, interaction,

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246

flexibility, focalization, creativity and quickness.

According to Cleland and Ireland, the participation in

a team increases the commitment and the fidelity of

the people, resulting in delivery of high quality, work

(Cleland and Ireland, 2000).

According to Moscovici, a high performance

team, besides all the requirements of a team as was

explained in the previous section, must have its

members must be committed to the personal growth

and success of each team member. Such a team will

exceed the performance of all the other teams and

achieve results above expectations (Moscovici,

2003).

Katzenbach and Smith (1993), present some

characteristics of high performance teams: “Deeply

personal commitments of each one to the growth and

the success of the others is what distinguish high

performance teams from the majority of the existing

teams. Energized by this extra sense of commitment,

the high performance team typically reflects a

vigorous amplification of the fundamental teams

characteristics: deeper sense of purpose, more

ambitious performance targets, a more complete

approach, more fullness in mutual accountability,

knowledge interchangeably and complementarity.”

Boyett and Boyett mention some companies that

have achieved great results with high performance

teams. The AT&T Credit Corporation has used high

performance interfunctional teams in order to

improve its efficiency and service to the client

(Boyett and Boyett, 1998).

According to Raj (Raj et al., 2006), it is noticed

that there is a major difficulty for an organization in

disseminating high performance team practices, such

as work reorganization, professional involvement in

decision making processes and improvement in

workers’s skills, despite the evidence that

organizations invest in these s practices to achieve

greater productivity and efficiency. Companies with

significant performance standards, according to

Katzenbach and Smith, stimulate and support high-

performing teams, helping them to establish their own

goals (Katzenbach and Smith, 1993).

3 TRAINING APPROACHES IN

Training in SE should prepare the students in both

theory and effective participation in a collaborative

and interdisciplinary environment. In this regard, it is

important consider the variation in training

techniques.

Traditional approaches in SE training are

considered to be (Anastasiou, 2004):

1. Dialogued expositive classes: This is a content

exposition, with active participation by the

students, whose previous knowledge must be

considered and can be taken as a foundation.

2. Text Study: This is an exploration of an author’s

idea from the critical study of a text and/or

information research and the author’s ideas

exploration.

3. Directed Study: This is study under guidance

and direction by the professor, aiming to solve

specific difficulties.

4. Use of a Discussion List: This is an opportunity

for group of people be able to debate, at a

distance, a theme in which they are experts or

have done a previous study.

5. Verbalization and Observation Groups

(VG/OG): This is an analysis of theme/problem

under a professor’s coordination that divides the

students in two groups: one for verbalization

(VG) and the other for observation (OG).

6. Seminar: This is a space where a group

discusses or debate themes or problems.

7. Case Study: This is the detailed and objective

analysis of a real situation that needs to be

investigated and that is challenge for the people

that are involved.

8. Workshop: This is the gathering of a small

number of people with common interests, which

aims to study and work for the knowledge and

deepening of a theme, under expert orientation.

These alternative approaches can help students to

learn more effectively. Alternative approaches are

considered to be (Prikladnicki et al., 2009) (Gresse

and Shull, 2009), (Monsalve et al., 2011), (Halma,

2014):

1. Group Activities, distance education and

practice activities: By using this approach,

interaction with the students is emphasized

through icebreakers that explore specific

subjects. The characteristics are: diversification

in the techniques for group activities; practical

classes in laboratories; the planning of the

student work; and part-time classes: 20% of the

discipline is done through distance education.

2. Capstone projects and practices activities: a

Capstone project is an approach where a student

group plans and executes a software project

from the beginning to the end during one whole

semester.

A Field Research on the Practices of High Performance Software Engineering Teams

247

3. Playgroup and games: For this strategy, related

content is first presented to the class. In the end,

in order to consolidate comprehension, a

playgroup is performed using LEGO®. The

game makes it possible to design, from the

defined requirements, a product to be built that

is similar to the software development.

4. Games and educational simulators: Because of

the need for training students in the SE process,

one of the alternatives is the use of games to fill

the gap between theoretical and practical

aspects. From the reports found in the literature

(Monsalve et al., 2011), it was noticed that the

majority of the proposals developed are

associated with simulator games.

The approaches that are more focused on the

students and that promote their further active

participation on the classes, for example with games

and simulators (Monsalve et al., 2011), (Halma,

2009), have the potential to increase the students

interest, motivate them and improve learning at level

of concept application.

4 FIELD RESEARCH ON HPT

PRACTICES

This field research was developed through interviews

with project managers from different companies, with

the following objectives:

• Evaluate the knowledge regarding the

performance of high performance teams in

the literature;

• Understand and activate skills for companies

to become each high performance

characteristic;

• Identify how training approaches are used to

improve the professionals in each practice.

4.1 Field Research Protocol

An exploratory, qualitative, non-experimental,

survey-type field survey was developed for a semi-

structured interview with open and closed questions.

The application of the questionnaire was made

through personal interviews. The following

procedures were developed:

a) Meetings to raise questions and structuring

the interview guide;

b) Review of interview guide;

c) Authorization of participating companies;

d) Validation of face and content;

e) Application of interviews.

The research respondents were project managers,

project leaders, and project coordinators. The

resources used were technological resources

(computer, text and spreadsheet software) and

materials resources (a meeting room in the

organization's own headquarters for half an hour, a

recorder to record interviews, paper and pen).

Data collection was done through semi-structured

interviews with open and closed questions. The

questionnaire will be applied with personal

interviews.

In the analysis of data, a critical analysis of these

results was made through the development of a

comparison of the results obtained with the theories

and related studies (Dutra et al, 2015). The interviews

were recorded and a qualitative analysis of the

collected data was carried out through a mapping of

the respondents' responses.

4.2 Field Research Execution

After we defined the research protocol, the field

research was executed.

In the face-to-face interviews, tape recorders were

used because according to Schraiber (1995), the use

of tape recorders in interviews is indicated to

amplifying the power of recording and capturing

extremely important communication elements,

pauses for reflection, doubts or intonation of the

voice, enhancing the understanding of the narrative.

Authors such as Patton (1990) agree with this

statement because the recorder preserves the original

content and increases the accuracy of the data

collected.

After all the interviews were carried out, each of

them was transcribed. As soon as the transcription of

the information was finalized, the analysis of the data

was started. According to Bardin (2004), the most

used form of treatment is Content Analysis, that

according to Oliveira (Oliveira et. al, 2003), consists

in the detailed reading of all the transcribed material,

in the identification of words and sets of words that

have meaning for the research, as well as in the

classification in categories or themes that have

similarity to the syntactic or semantic criterion. Still,

according to Olabuenaga and Ispizúa (1989), content

analysis is a technique for reading and interpreting the

content of all kinds of documents, which, if it is

properly analyzed, opens the doors to the knowledge

of aspects and phenomena of social life otherwise

inaccessible.

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In this field research, the Thematic Analysis

technique was used, which, according to Bardin

(2004), is inserted in the set of Content Analysis

techniques, whose objective is to highlight the items

of meaning from the description of the "corpus"

constructed based on the coding units or cut-out

categories of the content of interviews and

documents, which are guided by the problem and

objectives of this study (Bardin, 2004).

Bardin (2004), explains that this dialogue -

understood in the light of varied contextual categories

and information - makes interpreting as an intrinsic

element of the research process. Based on these

procedures, the different phases of analysis were

covered: (1) transcription and pre-analysis; (2)

floating reading and exploration of the material with

the establishment of categories; (3) data processing

from logical inference and interpretation; and (4)

confrontation and discussion of the results obtained

with the theories and related studies of the systematic

review of developed literature.

4.3 Field Research Results

Based on the research developed, we analyzed the

demographic data of the professionals interviewed,

according to Table1.

Table 1: Demographics of participants.

Age Respondents

21-30 1

31-40 5

41-50 14

Sex

Female 7

Male 13

Experience in Project

Management

1-5 years 4

6-10 years 3

11-15 years 12

16+ years 1

Most of the interviewees are male, between forty-

one and fifty years old, and work between eleven and

fifteen years in the activity of managing software

projects.

In turn, Table 2 gives us information about the

time and projects developed with a high performance

team. It shows if the interviewee worked on projects

in which a high performance team participated, how

much time worked with this team and how many

projects were developed.

Table 2: Time and projects developed in HPT.

Worked with HPT Number of Votes

Yes 19

No 1’

Time that worked with HPT

0-1 year

2-3 years

3+ years 0

Projects with HPT

1-5 projects 16

6-10 projects 1

10+ projects 2

4.3.1 Data Analysis

To determine which characteristics of a high

performance team are most relevant to the research,

we used 75% heuristics, that is, three quarters of

respondents should agree that the attribute is a high

performance characteristic, as shown in the following

table. This number is higher than that found in similar

studies in the literature, which suggest that when an

opinion is shared by at least 50% of the respondents,

it should be treated as a relevant impact opinion for

the study in question (Ali-Babar and Niazi, 2008).

Table 3 presents the characteristics of the high

performance teams, the number of votes for each

characteristic and their totals.

A Field Research on the Practices of High Performance Software Engineering Teams

249

Table 3: Ten most relevant characteristics.

# Attribute HPT’s

characte

ristic

(Yes)

HPT’s

characte

ristic

(No)

Know how to work

in a team 19 1 95%

2 Solid knowledge 18 2 90%

Effective

communication 17 3 85%

Efficient

coordination 17 3 85%

5 Diversity of skills 16 4 80%

6 Autonomy at work 16 4 80%

Organizational

commitment 16 4 80%

Unforeseen technical

challenges 16 4 80%

9 Self-manageable 16 4 80%

Confidence in their

own abilities 15 5 75%

The most selected characteristic in the field survey

was "know how to work in a team", with 19 votes,

followed by "have a solid knowledge", with 18 votes,

and 17 votes were to "have an efficient coordination"

and "have effective communication.

4.3.2 Categorization

Coding is the process by which raw data is

systematically transformed into categories, allowing

subsequent discussion of the relevant characteristics

of the content (Franco, 1986).

As Olabuenaga and Ispizúa (1989) said, the

process of categorization must be understood, in its

essence, as a process of data reduction. The categories

represent the result of an effort to synthesize a

communication, highlighting in this process its most

important aspects.

First, the data were prepared and, after the

transcriptions, a careful reading was made, in order to

seek the researcher's familiarity with the data before

starting the coding of the categories. In this coding

process, open coding and selective coding were used.

Open coding involves the breaking, analysis,

comparison, conceptualization, and categorization of

data. According to Bandeira-de-Mello and Cunha

Table 4: Practices for the characteristic: know how to work

in a team.

Know

how to

work in a

team

Categories Practices Found

Methodology 1. Develop team-wide

project scope discussions

through pre-planning and

pre-games

2. Develop agile teams,

perform Scrum ceremonies

with the project team

(planning, daily,

retrospectives), sharing

experiences, listening, trying

to help

3. Define a working

methodology

Team Building 1. Encourage and stimulate

teamwork

2. Develop HR integrations

3. Develop self-protection of

the team (the team protects

itself)

4. Use a team mailing list to

exchange messages

5. Make small celebrations

in the deliveries of the

projects

6. Work towards a common

goal by trying to help your

peers

7. Focus much more on the

whole than an individual

focus

Allocation 1. Make the allocation of the

team according to the

project's characteristic and

skills of the members

2. Have people working

physically close

3. Make new allocations

within the same project,

changing the context in the

middle of the project,

forcing a synergy between

the teams and focusing on

the need to work together

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(2006), in the initial stages of open coding, the

researcher explores the data by examining in detail

what seems relevant to him due to the intensive

reading of the texts. Table 4 exemplifies the initial

codifications of this research, and in this first stage

170 practices were found.

With the defined categories, it moves to an

intermediate level of abstraction, seeking the relation

between them to form the basis for its theoretical

construction. This process is called "axial coding". In

this stage, the number of practices was 135.

In the table 5, we have: (1) the main characteristic,

(2) the total of categories linked to this characteristic,

and (3) the total of practices extracted from the

interviews for the formation of high performance

teams in Engineering Software.

Selective coding is the final step in data analysis

and coding, and its purpose is to integrate and refine

the constructed categories. Selective coding is being

developed throughout the entire data collection and

analysis process, since integration is a continuous

process.

The final categories found in this field survey, and

this final step was completed with 106 practices.

The final categorization of the characteristics of

HPT found in this survey were:

1. Know how to work in a team: Methodology,

Team Building and Allocation

2. Solid knowledge: Knowledge and Formation

3. Efficient coordination: Team Management,

Methodology, Roles and responsibilities and

Communication

4. Effective communication: Methodology,

Communication, Allocation, Feedback,

Management and Tools

5. Organizational commitment: Commitment,

Strategy, Team Building and Feedbacks

6. Diversity of skills: Knowledge, Feedback, Team

formation, Rotation and Exchange of

experiences

7. Self-manageable: Energy, Autonomy,

Communication and Methodology

8. Autonomy at work: Autonomy, Leadership and

Methodology

9. Unforeseen technical challenges: Knowledge,

Skills and Contingency Analysis

10. Confidence in their own abilities: Roles and

responsibilities, Knowledge and Recognition

From the identification of the categories linked to

the characteristics of the high performance teams, the

data collected were extracted from the interviews,

categorized, and from the identification of the

categories, the practices were listed. This field

survey, for ten high performance characteristics,

totaled 106 high performance team practices in 37

categories, as shown in Table 5.

Table 5: Number of characteristics x categories x practices.

Characteristics Categories Practices

1. Know how to work in a

team 3 13

2. Solid knowledge 2 7

3. Efficient coordination 4 14

4. Effective communication

6 16

5. Organizational commitment 4 11

6. Diversity of skills

5 10

7. Self-manageable 4 11

8. Autonomy at work

3 5

9. Unforeseen technical

challenges 3 11

10.Confidence in their own

abilities 3 8

Total

37 106

5 CONCLUSIONS

As a conclusion of this field research, we can say that

of the three objectives defined for the research, only

two were successfully completed.

Objective 1, to evaluate the knowledge of the

professionals regarding the characteristics of the high

performance teams found in the literature, was

successfully achieved, since the characteristics of the

high performance teams found in the Systematic

Review of Literature (Dutra et al, 2015), were cited

by the interviewees as characteristics of high

performance teams.

Objective 2, to understand and identify the

practices companies use to develop each high

performance characteristic, has also been

A Field Research on the Practices of High Performance Software Engineering Teams

251

successfully completed. As shown in Table 5, 106

practices of high performance teams were identified

in 37 different categories.

The ultimate goal of this field research was to

identify the training approaches that are used to

enhance people in each identified practice, but this

goal was not successfully completed. That is because,

it was not possible to find such approaches in the data

extracted from the interviews. In most cases, the

interviewees focused on mentioning what training

was made for a particular practice rather than the

training approach that was developed. Considering

this difficulty in extracting the approaches, a new

study was developed with the objective of searching

in the literature the training approaches, the

characteristics of the high performance teams most

cited in field research.

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