Industry-Academy Collaboration in Agile Methodology: Preliminary

Findings of a Systematic Literature Review

Denis de Gois Marques

, Tâmara D. Dallegrave

, Luis E. L. Barbosa

Cleyton Mario de Oliveira Rodrigues

and Wylliams Barbosa Santos

Polytechnic School of Pernambuco, POLI/UPE, Recife, Brazil

University of Pernambuco, UPE, Pernambuco, Brazil

Keywords: Industry-Academia Collaboration, Agile Software Development, Systematic Literature Review, Software

Engineering.

Abstract: Collaborative Research between Industry and Academia (IAC) in Software Engineering (SE) is being applied

and developed in practice. Collaborative practices help both environments, from academic and software

industry perspectives. As a way of observing what is being developed in the SE, the objective of this article

is to present an exploratory and empirical study of IAC practices in the scope of Agile Software Development

(ASD), exploring and characterizing solutions and practices, the challenges found in the application of the

IAC and the collaboration. A Systematic Literature Review (SLR) was carried out in five main academic

databases, evaluating/analyzing 7143 articles, totalling 12 articles approved following the proposed criteria.

As preliminary findings of the data analysis, 76 good practices and 37 challenges in carrying out the IAC

were described. As well, practical models for the application of IAC were detailed.

1 INTRODUCTION

With a relevant quantity of practitioners, the Industry

and Academic communities in the Software

Engineering (SE) field are very large and diverse.

Due to the importance of these two areas, the

cooperation between them is very important for the

enhancement of software development. Nevertheless,

these two areas are usually disconnected (Glass,

2006; Garousi et al., 2016), with a low amount of

researchers and practitioners collaborating with the

other community. This practice of Industry-

Academia Collaboration has a very important impact

on SE, in which communities can identify each

other’s needs and develop cooperation strategies for

those needs.

Agile methods usage is a consensus between

practitioners at developing software and also very

discussed in the academic community (Dings

yr et

al., 2012). Fostering this knowledge exchange can

https://orcid.org/0000-0002-5351-2232

https://orcid.org/0000-0001-9431-565X

https://orcid.org/0000-0002-0769-1540

https://orcid.org/0000-0003-3816-656X

https://orcid.org/0000-0003-2578-1248

bring success for both communities, since there is a

better understanding on the part of researchers of the

needs of practitioners, ensuring competitiveness for

organizations.

Over the past few years, there has been an increase

in software development practices, requiring changes

and refinements in the software development process.

Several software development practices have been

implemented and evaluated in the software industry

(Boehm, 2006). Among the solutions, Agile Software

Development (ASD) stands out as a useful, low-effort

practice that presents a reduction in the failure rate in

software development (Dyba and Dings

yr, 2008a).

As academia and industry collaborate on projects

that are applicable (Ven, 2007) (such as publishing,

funding and academic practice vs. new technologies

and industry project success), it is important to

discover the challenges and propose practices to

facilitate these collaborations.

Marques, D., Dallegrave, T., Barbosa, L., Rodrigues, C. and Santos, W.

Industry-Academy Collaboration in Agile Methodology: Preliminary Findings of a Systematic Literature Review.

DOI: 10.5220/0011115900003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 191-198

ISBN: 978-989-758-569-2; ISSN: 2184-4992

191

However, the number of collaborations between

these communities is still considered low, resulting in

gaps in the literature of studies involving IAC and

Agile Software Development (ASD). In addition,

industry and academia collaboration start from a

problem that is not well defined, work in an

environment of constant change and objectives are

planned by iterative steps during project execution.

The similarities discussed present good evidence for

the observation of agile methodologies practices in a

collaborative environment between industry and

academia. The goal of this study is to carry out an

Systematic Literature Review (SLR) of articles that

present perspectives of collaboration between

industry and academia in the Agile Software

Development (ASD) context, focusing on identifying

practices, challenges and models of IAC projects.

2 THEORETICAL REFERENCE

In this section, the main concepts necessary to

understand the article were described, being about

Agile Software Development (ASD) and Industry-

Academy Collaboration (IAC).

2.1 Agile Software Development

In the article by Dyba and Dingsøyr (2008b), a

systematic review of empirical studies on agile

software development was carried out, with the goal

of evaluating four themes: Introduction and adoption,

human and social factors, perceptions about agile

methods and comparative studies. From this review,

1996 papers were identified, of which 36 were

identified as empirical studies and analysed. The

review investigated the benefits, limitations and

strengths of evidence of agile methods, through the

analysis of the articles.

The global software industry requires continuous

improvement to remain competitive and respond to

rapid growth without losing software quality. In this

process of reacting to these global software changes,

agile methods represent a remarkable and widely used

solution in today's industry (Kamei et al., 2017). The

use of agile methods is a successful approach to

software development, due to its flexibility and low

maintenance effort, as well as the quality and speed

of development (Chookittikul et al., 2011) (Santos et

al., 2017).

2.2 Collaboration Industry-Academia

The integration and collaboration of academia with

industry provides a unique learning environment,

where researchers meet new industry insights in real-

world environments, and the industry develops new

technologies and solves problems within the

company (Steglich et al., 2020; Garousi et al., 2017;

Barbosa et al., 2020; Dallegrave et al., 2021).

One of the greatest challenges in the industry-

academia collaboration is the adverse mentality of

industry compared to academia, industry focuses on

building and selling products and academia focuses

on new knowledge and fundraising (Sandberg et al.,

2011).

To investigate this process, Wohlin et al. (2012)

conducted a survey with 48 researchers and 41

professionals, in Sweden and Australia, to observe

and investigate the success factors of IAC practices in

Software Engineering in general. Among these

factors, we highlight: buy-in and support from the

company’s management; differences in goals among

collaboration participants; and social skills are

important and necessary, particularly in long-term

collaboration.

The article by Santos et al. (2016) demonstrates

the considerations about carrying out a research and

development (R&D) project, about the applicability

and experience of adopting agile practices in a

collaborative project. In this project, the industry's

need for shorter iterations, application of UML

models and deliveries and team management, caused

the project make organizational changes to agile

practices, which was “crucial to managing

expectations” of the industry.

3 RESEARCH METHODOLOGY

To conduct the systematic literature review (SLR),

the well-established guidelines in software

engineering defined by Kitchenham et al. (2007) and

Petersen et al. (2015) were used, focusing on the

research syntheses and the description of the

challenges and practices applied in these researches.

Figure 1 shows the scope of the research. The purpose

of this study is to provide an insight into the

challenges and practices carried out in the articles

analysed, with a focus on agile practices. Thus, the

following research questions were applied:

RQ1: What challenges to the application of IACs in

ASD were raised?

RQ2: What are the proposed practices for improving

IAC in the context of ASD?

RQ3: What types of IAC models have been proposed

in the context of ASD?

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To search for relevant studies, five main search

engines are used, namely: ACM Digital Library,

IEEE Explore, Science Direct, Scopus and Springer.

The final search string was based on two seminal

works in the context of IAC (Garousi et al., 2016),

and agile software development (Dings

yr et al.,

2012).

Figure 1: Methodology.

In order to carry out the systematic literature

review, the protocol was structured in four phases:

• Phase 1 - Automatic Search: consists of

searching for articles in the databases;

• Phase 2 - Pre-selection: is pre-selection, which

involves reading the titles and abstracts of the

articles;

• Phase 3 - Selection: is carried out by applying the

inclusion and exclusion criteria, reading and

analyzing the introduction and conclusion of the

articles; Based on the research questions, the

criteria for inclusion (IC) and exclusion (EC) of

articles were defined:

IC1) Articles that answer the research questions;

IC2) Articles that meet the quality criteria.

EC1) Articles published from 2010 to 2021;

EC2) Incomplete articles, secondary and tertiary

studies, abstracts or slideshow;

EC3) Articles not written in English;

EC4) Articles that do not answer the research

questions;

EC5) Articles unavailable electronically;

EC6) Duplicate articles; and

EC7) Articles that do not meet the quality criteria.

• Phase 4 - Analysis: in the last stage, in order to

recover relevant articles, some quality criteria

(QC) were defined, following the guidelines of

Dyba et al. (2007), namely:

QC01) Is the theme of IAC addressed directly?

QC02) Is there a case analysis in the company?

QC03) Is it a real project with a real customer?

QC04) Is the researcher inserted in the context,

working in the company?

QC05) Are there changes in the environment with

the insertion of the researcher?

QC06) Clearly answer the survey questions?

In this phase, the approved articles were read

completely, leading to the set of primary studies of

this review (Appendix).

4 RESULTS AND DISCUSSION

In this section, the preliminary findings of the data

analysis are presented. In this way, the data are

presented according to the research questions. The

number of articles researched and the final results are

shown in Table 1.

Table 1: Number of Articles Analyzed.

Database Phase 1 Phase 2 Phase 3 A

rove

IEEE 730 47 18 2

ACM 97 19 5 1

Science

Direct

613 72 10 1

Sco

us 3516 142 29 3

rin

2187 159 43 5

Total 7143 439 105 12

From the primary studies found on the literature

review, studies available in the appendix, the data

coding and categorization process was started.

Electronic spreadsheets were used for the

construction simplified results, and for the

construction of the categories, a qualitative analysis

assistance software, Atlas TI, was used.

The analyzes and coding are built following the

principles of Charmaz (2006), where Open and Axial

coding were used to develop subcategories and

categories in the data analysis.

Within these analyzes, 10 categories were created

highlighting the challenges and impediments of

collaborations (RQ1), 14 categories were created

highlighting the best practices and patterns regarding

the industry-academia collaboration process (RQ2),

(Industry OR Practice OR University OR

Academia OR Theory OR Collaboration OR

Relationship OR Relation) AND ("Agile

Development" OR "Agile Methodologies" OR

"Agile Software Development" OR "Agile

Methods" OR "Agile Projects" OR "Agile Project

Management" OR Scrum OR ScrumBan OR "Extreme

Programming" OR "Lean Software Development" OR

Industry-Academy Collaboration in Agile Methodology: Preliminary Findings of a Systematic Literature Review

193

in addition to presenting collaboration models for the

practice of IAC (RQ3).

In the link (shorturl.at/uEMV7) it is possible to

observe the research protocols, the approved articles,

the demographic data and the list of categories and

subcategories of each of the research questions.

4.1 RQ1: What Challenges and

Impediments to the Application of

IACs in ASD Were Raised by

Newspapers?

This question analyzes the challenges and

impediments found in the analyzed articles. As result,

10 categories and 37 sub-categories (labelled by CI)

were highlighted. Thus, the main categories are

represented in the Table 2:

Table 2: Representation of the categories of Challenges in

the application of IAC.

C01: Incompatibility between industry and

academia

C02: Research Metho

C03: Lack of Training, Experience and Skills

C04: Lack of interest or Low Commitment

C05: Problems related to Communication

C06: Human and Or

anizational Factors

C07: Issues related to Agile Practices

C08: Issues related to Management

C09: Resource-related Issues

C10: Contractual and Privac

Concern

Category 01 represents the incompatibility

between industry and academia, where they represent

the contrasts of reality on both sides. This category

describes the different perceptions of the

collaboration fields, where they demonstrate different

interests and objectives (CI06), perceptions of

challenges (CI15) of solutions and of different results

(CI25). In addition, they represent differences

between the industry and academia schedule (CI04)

and difficulty in synchronizing schedules (CI03).

This category is represented by 10 subcategories.

Category 03 is represented by the lack of training,

experience and skills of project employees. Thus, the

lack of skill and experience in Software Engineering

(CI21) and Agile (CI20) are factors that hinder these

collaborations, as well as the difficulties in

collaborating with the researchers’ solutions (CI16),

where training in both contexts are necessary (CI20

and CI21). This category is made up of only 3 sub-

categories.

The lack of proper communication between the

members of a collaboration is one of the main

challenges to leverage collaborations. In Category 05,

3 subcategories are presented that represent this

knowledge. The insertion and availability of the

researcher at the collaboration site (CI12) and the

communication gaps between communities (CI26)

are challenges for the practice of collaboration.

Another form of communication challenge is

concealment or difficulty in obtaining information

from the team (CI37).

Agile practices, in Category 07, is the main point

of the research project, so agile practices also present

difficulties in applications in collaborative projects.

The difficulties involve the difficulty of following the

iterations (CI09), the interruption of the iterations

(CI10) and short sprints (CI11) of the project. In

addition, some projects are resistant to changes from

traditional approaches to agile (CI33). This category

is made up of only 3 subcategories.

In this section, 4/10 categories were presented that

represent the challenges and impediments for a

collaborative project, in addition to the

representations of the subcategories. The categories

presented represent the highest degrees of

“groundedness”, the other categories and

subcategories are exposed in the link (available in the

results section).

4.2 RQ2: What Are the Proposed

Practices for Improving the IAC in

the Context of Agile Software

Development (ASD)?

This question analyzes the good practices that were

developed and represented in the analyzed articles. In

total, 14 categories and 76 subcategories (labelled by

GP) of practices were constructed in the development

of an IAC. In Table 3, it is possible to observe the

categories constructed.

In Category 01 (Knowledge Management), good

knowledge management practices of the

collaborating teams are described, such as

communication skills, training and social and

research skills. Some practices that were described in

this category are: holding seminars and workshops

(GP31), conducting training (GP54), promoting

satisfaction with learning (GP24) and developing

social and management skills (GP42). In addition to

these four practices, four more practices were also

developed, summarizing 8 subcategories.

Category 02 (Project Management and

Engagement Assurance) focuses on the collaboration

and involvement of project participants. Thus, it is

necessary to meet the needs of the industry (GP35),

ensuring the involvement of the industry (GP11), the

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transfer of knowledge (GP16) between employees

and the development and encouragement of the

industry (GP03). This category is represented by 8

subcategories.

Table 3: Representation of good practice categories for

collaboration.

C01: Knowledge management (communication,

trainin

and skills

)

C02: Ensuring engagement and project management

C03: Considers industry needs, challenges, goals

and

roblems

C04: Ensuring benefits for the industry and solving

roblems

C05: Maintainin

relationshi

s and understandin

C06: Software A

ilit

C07: Teamwor

C08: Manage Risks and Limitations

C09: Researcher Available and Accessible

C10: Manage Financing / Recruitment / Partnership

and Hiring

C11: Measurement and Evaluation

C12: Support Tools Search

C13: Test / Pilot Solutions

C14: Provide tool su

ort for solutions

In Category 03, categories are represented that

consider the needs, challenges, goals and problems in

the industry, whether in the construction of the

research project or in the execution of the project.

Thus, problems based on real facts (GP43) and the

involvement of professionals in the construction of

the problem (GP12) are important factors in

collaborations. But also, the research must meet the

needs of the industry, as well as that of the researcher

(GP18). This category is represented by 8

subcategories.

Aspects related to relationships, social skills and

collaborative behavior are represented by Category

05, which presents 8 subcategories on these practices.

The socialization of the proposal in the organization

(GP02), the close contact of the researcher in the

place of collaborative practice (GP05) and the

establishment of trust and satisfaction (GP20) are

fundamental practices in this category. In addition to

these, planning meetings, whether daily or periodic,

should be pursued (GP06).

The agile aspects is the central point of the

research, where the observation and analysis of these

practices are very suitable for the context of IAC. In

Category GP06, the contexts of Software Agility

aimed at collaborations were presented. This category

is represented by 15 subcategories.

The inclusion of agile practices in collaborative

projects (GP32) act in very positive ways, such as

managing expectations Santos et al. (2016) and

converting large projects into smaller projects

(GP30). In addition, the use of short iterations, short

cycles and sprints (GP26) are one of the main factors

in the insertion of agile methodologies. The Sprint

Retrospective (GP62), Planning Ceremony, Sprint

Review (GP40), Daily Meeting and/or Planning

Meeting (GP07) and Daily Standup (GP69) practices

are well applied in the contexts studied.

Agile skills such as Spike Solution (GP56), Pair

Programming (GP57), Planning Poker (GP61) and

User Story (GP21) were also reported in the

development of collaborations, these skills being

related to team training, project execution,

competence levelling (described in Category 01,

GP59) or in the construction of the

research/development proposal.

In this section, 5/14 categories were presented,

with the archiving of some subcategories that

represent the knowledge of the respective category,

the categories that present higher degrees of

“groundedness” were described. The other categories

and subcategories are presented in the link (available

at the beginning of the results).

4.3 RQ3: What Types of IAC Models

Have Been Proposed for the

Context of ASD?

Studies such as those by Garousi et al. (2016) and

Marijan and Gotlieb (2021) describe the application

of case studies and action research and present

collaboration models for IAC practices, such as the

“Certus Model” and the “Spiral Model”.

Collaboration models determine the practical

structure and roles of project participants (Marijan

and Gotlieb, 2021). In the analysis, five models of

collaboration were described for the practice of IAC

in agile contexts.

The research paper authored by Munch et al.

(2013), the authors provide a “technology transfer

model” that is directly related to the construction of

a Minimum Viable Product (MVP) in academia and,

after the construction, the transfer to the domain of

industry. Among the steps used to apply the model

are: i) Identify problems on industry by a case study;

ii) Formulate a solution for the problem, with the

industry cooperation; iii) Make validations of these

solutions; and iv) disclose the step by step for

implementation in industry.

In Choma et al. (2015a) is presented the

Cooperative Method Development (CMD) models,

which has as main characteristics the action research

principles, with an approach more qualitative and

Industry-Academy Collaboration in Agile Methodology: Preliminary Findings of a Systematic Literature Review

195

combining with problem-oriented methods. The

CMD application cycles are: i) Understanding the

practice; ii) Deliberate improvements; And iii)

Implement and observe improvements.

Making a junction with CMD with Design

Science Research (DSR) practices for the conduction

of a collaboration, the article authored by Choma et

al. (2015b) presents the methodology “SoftCoDer”

based on this junction of principles. In the work of

Choma et al. (2016), the approach “SoftCoDer UserX

Story: Incorporating UX Aspects” is presented, which

brings the foundation of the CMD, with guidelines

from the DSR, such as: “design artifacts of value

based on both real need of industry (relevance) and

scientific knowledge (rigor of research)”.

Also using the action research methodologies, the

paper authored by Babb et al. (2014) presents the

Dialogical Action Research (DAR), which is an

emerging and engaged approach in both research and

practice, where it is designed to promote an

understanding of applications to practical

phenomena. As a form of collaboration, the DAR

presents “a researcher/practitioner partnership that

allows for a reflective dialog to explore and shape

change in an organization and also specify learning

for a scientific community”.

In the study authored by Sandberg et al. (2011) is

presented the concept of Collaborative Practice

Research (CPR), where the concepts of practitioners

(“insiders”) and researchers (“outsiders”) are applied

who work in close collaboration, where they take

advantage of bringing their knowledge in

identification, analysis and interpretation together in

the project.

5 DISCUSSION AND VALIDITY

In this section, discussions about the results found are

presented, as well as threats to the validity of the

study.

5.1 Discussion

The research is based on describing factors that

influence the practice of collaborations between

industry and academia, where through the factors

presented in the research, it is possible to enhance

new collaborations and reduce potential failures.

One of the main practices, based on the number of

citations in the analysis, was the insertion of the

researcher in the industrial context, carrying out this

exchange of knowledge. The researcher in the

inserted context is free to collect evidence and

experiences, feelings and difficulties of the

participants, to build insights, hypotheses and

solutions for the environment. All these elements

should be discussed openly between the collaborating

parties, which is good practice in these collaborations.

For a better interposition of researchers in the

context of the industry and a successful collaboration

between the communities, an excellent and frequent

practice in the analysis of the data is the

accomplishment of training and leveling of

knowledge between practitioners and researchers.

However, challenges such as lack of time and

incompatibility of agenda for building new

knowledge can be found in conducting collaboration.

Having as a reference the incompatibilities of

schedules, interruptions of iterations and time

windows for the research, the agile practices present

a good way out for these problems. Agile practices

such as short iterations, sprint review and planning

meeting react very well to changes in the

environment.

The models described in RQ3 are important ways

on how to execute an IAC in some context. From

these models, it is possible to repress risks to the

performed researches. As presented in RQ3, there are

several models that propose the execution of

collaborative practices, such as the certain model

(Marijan and Gotlieb, 2021), spiral model (Rombach

and Achatz, 2007), collaborative models aimed at

agile research (Sandberg et al., 2011; Sandberg and

Crnkovic, 2017) and, the one that has been gaining a

lot of space in the research, the technology transfer

model (Gorschek et al., 2006; Mikkonen et al., 2017).

5.2 Threats to Validity

This section discusses some threats to research

validity, namely:

• Research carried out by two researchers, where a

third and a fourth researcher acted as support and

data validation;

• Searches performed in the five main automatic

databases, but not applied to searches in manual

databases.

• Each data repository has its own search processes,

so it was necessary to adapt the string in each of

the repositories;

• The quality ratings of the articles were based on

the “five levels of closeness” proposed by Wholin

(2013) and described by Garousi et al. (2019). In

this way, stricter criteria were placed as defined

by the authors on IAC.

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6 CONCLUSIONS

Collaborations between industry, academia and

government have the power to leverage both fields,

academically and industrially, in a very positive way.

The inclusion of research collaborations tends to help

in the formation of more qualified researchers and to

make the industries more adequate to the technologies

and to enhance the processes.

The objective of the research is to carry out an

exploratory review of articles that present a

perspective of collaboration between industry and

academia (IAC), in a software agility perspective. To

perform these analyses, a Systematic Literature

Review (SLR) was conducted on five main search

engines.

As preliminary findings of the systematic review,

and with the performance of a qualitative analysis on

the articles evaluated, 10 categories and 37

subcategories were built on the challenges and

impediments of the execution of IAC, and 14

categories and 76 subcategories that express the good

practices on the execution of collaborations between

industry and academia.

In addition, 5 models were exposed for carrying

out an IAC. Agile practices in collaborative projects

(IAC) are very adherent due to the need to

demonstrate fast and qualified results, this is widely

applicable using short iterations, quick meetings and

short sprints.

As future perspectives, our next step is to validate

the results with professionals in the field and design

an ontology model relating the challenges with the

good practices in the context of the IAC. Therefore,

this conceptual model may serve to organize and

structure the domain, through which professionals

can perform reasoning task in order to infer a set of

practices to be introduced within a specific IAC

project based on the identified challenges.

ACKNOWLEDGEMENTS

This study was financed in part by the Coordenação

de Aperfeiçoamento de Pessoal de Nível Superior –

Brasil (CAPES) – Finance Code 001.

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APPENDIX – INCLUDED STUDIES

[S1] Ardito et al. (2017). Integrating a SCRUM-Based

Process with Human Centred Design: An Experience from

an Action Research Study. doi: 10.1109/cesi.2017.7 [ID01]

[S2] Pino et al. (2010). Using Scrum to guide the execution

of software process improvement in small organizations.

doi:10.1016/j.jss.2010.03.077 [ID04]

[S3] Munch et al. (2013). Creating Minimum

Viable Products in Industry-Academia Collaborations.

doi:10.1007/978-3-642-44930-7 9 [ID06]

[S4] Choma et al. (2015). Communication of Design

Decisions and Usability Issues: A Protocol Based on

Personas and Nielsen’s Heuristics. doi: 10.1007/978-3-319-

20901-2 15 [ID07]

[S5] Santos et al. (2016). Using Scrum Together with UML

Models: A Collaborative University-Industry RD Software

Project. doi: 10.1007/978-3-319-42089-9 34 [ID09]

[S6] Wen et al. (2018). FLOSS Project Management in

Government-Academia Collaboration. doi: 10.1007/978-3-

319-92375-8 2 [ID10]

[S7] Choma (2016). UserX Story: Incorporating UX

Aspects into User Stories Elaboration. doi: 10.1007/978-3-

319-39510-4 13 [ID11]

[S8] Choma et al. (2015). Towards an Approach Matching

CMD and DSR to Improve the Academia-Industry

Software Development Partnership: A Case of Agile and

UX Integration. doi: 10.1109/sbes.2015.18 [ID12]

[S9] Sandberg et al. (2017). Meeting Industry-Academia

Research Collaboration Challenges with Agile

Methodologies. doi: 10.1109/icse-seip.2017.2 [ID13]

[S10] Sousa et al. (2016). Using Scrum in Outsourced

Government Projects: An Action Research. doi:

10.1109/hicss.2016.672 [ID14]

[S11] Babb et al. (2014). XP in a Small Software

Development Business: Adapting to Local Constraints. doi:

10.1007/978-3-319-09546-2 2 [ID15]

[S12] Nalepa et al. (2019). Using Agile Approaches to

Drive Software Process Improvement Initiatives. doi:

10.1007/978-3-030-28005-5 38 [ID17]

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