A Grey Literature Review on the Impacts of Covid-19 in Software

Development

Everton Quadros

, Rafael Prikladnicki

and Regis Lahm

PPGCC, Polytechnic School, PUCRS University, Av. Ipiranga, 6681, Porto Alegre, Brazil

Keywords:

Remote Work, Distributed Software Development, Software Engineering, Covid-19, Pandemics.

Abstract:

The workplace has been changed by Covid-19. But what is the meaning of the “work from home” phenomenon

in software development? This paper aims to investigate the “work from home” pandemic phenomenon in

software development. Between October 2019 and December 2021, the Grey Literature review was carried out

to investigate 25,251 records, collected through a scraper written in python language. Descriptive analysis was

performed using data science and artiﬁcial intelligence techniques. We developed a methodology to optimize

the collection and extraction of insights from the Grey Literature and reveal perceptions or cognitive distances

from the social representation of the impacts of Covid-19 in software development. The main contributions

of this paper are to show how Grey Literature may contribute to anticipate ﬁndings, reveal changes in the

discourse regarding the effects of the pandemic on the work model, and show that in early 2021 the desire for

ﬂexibility pressed for a hybrid model. This type of literature review can assist in strategies to deal with events

such as Covid-19.

1 INTRODUCTION

The Covid-19 pandemic was made ofﬁcial on March

11, 2020 by the World Health Organization (WHO)

(Bogoch et al., 2020). Since then, studies that analyze

the impacts of the pandemic in the area of Software

Engineering (SE) have begun (Ralph et al., 2020;

Oliveira et al., 2020). This paper aims to character-

ize the state-of-the-art about impacts of the pandemic

on the working model of SE through a Grey Literature

(GL) review (Garousi et al., 2019; Kamei et al., 2021).

We also proposed the development of a methodology

that optimizes the collection and extraction of insights

from GL to answer the following research questions:

• RQ1: How to automate the collection of evidences

from the grey literature to investigate the impacts

of Covid-19 in software development?

• RQ2: What are the cognitive distances in the so-

cial representation of the impacts of Covid-19 in

software development?

To answer these questions we resorted to the sci-

entiﬁc method of Systematic Literature and Grey

https://orcid.org/0000-0003-4137-9937

https://orcid.org/0000-0003-3351-4916

https://orcid.org/0000-0002-1102-5655

Literature (GL) reviews (Kitchenham and Charters,

2007; Garousi et al., 2019). Using a Python scraper

algorithm we collected from Google Search Engine

(GSE) the GL records for the period between October

2019 and December 2021. This process generated a

corpus with 25,251 records. After the screening (Page

et al., 2021) -procedures for selecting a study by

relevance (Kitchenham and Charters, 2007; Garousi

et al., 2019) and data extraction -procedures for an-

swer research questions (Kitchenham and Charters,

2007; Garousi et al., 2019), data science and artiﬁ-

cial intelligence were applied to create data synthesis

-procedures for synthesizing evidence with qualitative

analysis (narrative) or quantitative analysis (Kitchen-

ham and Charters, 2007; Garousi et al., 2019), speed-

ing up the synthesis of results. The choice to use

GL resulted from the urgency of the research and the

scarcity of data on the impact of pandemics in soft-

ware development in the Academic or Traditional Lit-

erature, according to the guidelines for GL (Garousi

et al., 2019).

The objective of RQ1 was to develop a method-

ological proposal that automates the process of data

collection and data synthesis in GL. RQ2 aimed to re-

veal perceptions or cognitive distances -distance be-

tween different perceptions about an object or phe-

nomenon (Nooteboom et al., 2007) from the social

Quadros, E., Prikladnicki, R. and Lahm, R.

A Grey Literature Review on the Impacts of Covid-19 in Software Development.

DOI: 10.5220/0011116100003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 199-206

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

199

representation (Moscovici, 2007; Jodelet, 2001) of

the impacts of Covid-19 in software development.

This RQ2 is important to understand how Covid-19

(as an extreme event) can impact different aspects of

software development, for example: disrupting the

software development working model through “new

normals”.

This paper is organized as follows: Section 2

presents the background. Section 3 describes the

methodology used. Section 4 reports the results. Sec-

tion 5 presents the discussion and Section 6 presents

the conclusions and ﬁnal considerations.

2 BACKGROUND

Until recently, there were two main work models, ﬁrst

“work from home” and then “back to the ofﬁce” (Mi-

crosoft, 2021a) (Meira, 2021). However, the effects of

the pandemic were far from being binary. Now a third

way has been emerging since early 2021. However, it

is common knowledge that the discussion about a fu-

ture of remote work is not new.

Domenico de Masi wrote back in 1993 her book

called “Il telelavoro (Telework)”, where the author an-

alyzed 10 years of case studies about the challenges

and beneﬁts of telework. In 1999, in the book “The

future of work”, he named Chapter 17 ‘Working Any-

where” (de Masi, 2021). This title alone makes clear

the author’s vision, already at that time, that the “fu-

ture of work” would be distributed or remote.

A brief review of the software engineering litera-

ture also reveals that distributed or remote is nothing

new and the present authors are part of a study group

that has been investigating the development of dis-

tributed software since the mid-2000s (Prikladnicki,

2003). If distributed software development, based on

the very concept of remote work, has not been new

for more than 20 years, how has Covid-19 really im-

pacted SE?

Answers are coming from social representations

or perceptions (Moscovici, 2007; Jodelet, 2001) of

professionals in the SE value chain about the impacts

of Covid-19 on the work model. Analyzing their per-

ceptions, verifying if there are cognitive distances and

what they would be, in order to arrive at a social rep-

resentation of the phenomenon itself is the challenge

of this paper.

Primary studies have addressed the impact of

Covid19 on the productivity of software engineers,

highlighting here one at the national in Brazil level

(Oliveira et al., 2020) and another at the international

level (Ralph et al., 2020). These were conducted

through a survey online. The studies showed diver-

gent social representations about how Covid-19 im-

pacted the productivity of software engineers. The

international study reported a loss of productivity (re-

sulting from the lack of an adequate structure for

forced remote work) and the Brazilian study reported

an impact of increased productivity (resulting from

fewer interruptions throughout the day). We iden-

tify in this case that there are different representations

of the same phenomenon that is the very impact of

Covid-19 in SE.

For Serge Moscovici and Denise Jodelet, social

representations are considered a phenomenon capa-

ble of materializing the world of ideas, mobilizing the

subject from the unknown to the familiar, therefore,

from the abstract to the concrete (Moscovici, 2007;

Jodelet, 2001). For Jodelet, social representation is

related to the basic needs of apprehension and com-

munication of/in the world, stating that we create rep-

resentations out of the need to be informed about the

world around us, emphasizing that they help us name

and deﬁne the most different aspects of daily life, in

order to interpret, make decisions and position our-

selves in relation to them.

Social Representations explain how and why peo-

ple give different names and concepts to the same

phenomenon. For example, Domenico de Masi’s tele-

work was cited as “Flexible Working Arrangements”

by the Human Resources team of UN (UN, 2019).

According to The Federation of International Civil

Servants Associations (FISCA) “The Future of the

UN System Workforce, or the UN Future of Work, is

a Task Force launched by the High-Level Committee

for Management (HLCM) during its 38th Session, 15-

16 October 2019”. In it, ﬂexible work arrangements

(FWA) represent the modern workplace (UN, 2019).

From this point of view, the data in this GL are so-

cial representations or reported perceptions about how

they understand the impacts of Covid-19 on SE. Car-

rying out this analysis means identifying what are the

similarities, what are the trends evidenced by the re-

ports, therefore, if there are cognitive distances or dif-

ferent views on the phenomenon and what they would

be. What are the views on how Covid-19 impacts the

SE work model? The main objective here is to an-

swer this question. We will now describe the methods

used.

3 METHOD

The Grey Literature review (GL) was performed by

means of GL guidelines (Garousi et al., 2019; Kamei

et al., 2020). Herewith, we deﬁned the Search pro-

cess (Section 3.1), Source selection (Section 3.2),

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Study quality assessment (Section 3.3), Data extrac-

tion (Section 3.4) and Data Synthesis and Classiﬁca-

tion (Section 3.5).

3.1 Search Process

Following Kitchenham and Charters (Kitchenham

and Charters, 2007) the search strings of the system-

atic mapping were organized in terms of population,

intervention, and expected results. The comparison

was omitted here because it is not relevant to the

present goal. Stabilization tests were performed un-

til the ﬁnal adjustment of the search strings. The ﬁnal

format used was:

• Population (software engineers or software devel-

opment or disaster informatics);

• Intervention (productivity or work from home or

remote working);

• Result (covid impact or disaster or resilience).

As proposed by (Kitchenham and Charters, 2007),

table 1 presents a summary of the search strategy

adopted, the inclusion criteria (IC) and, exclusion cri-

teria (EC) - which are described in depth in subsection

3.2.

Table 1: Data source and Search strategy.

Database CI/CE Search

title

Google Search ”Criteria” summary

Engine for selecting key words

(GSE) a study” date

The challenge in searching GL is choosing the

data source. There are many alternatives. Some au-

thors use project versioning platforms like GitHub.

Others use social networks for technical support to

developers such as Medium or StackOverﬂow. There

is also the possibility to focus on newspaper news.

Any choice impacts the result. To facilitate a broad

spectrum search, it was chosen to use Google search

results, as it can give an overview of the topic (Souza

et al., 2021; de Oliveira et al., 2021).

3.2 Source Selection

For each record collected, a set of inclusion and ex-

clusion criteria were applied. To be included, each

study must meet all inclusion criteria. The Table 2

describes the inclusion criteria. Exclusion criteria, on

the other hand, are described in Table 3. We excluded

any study that met at least one exclusion criterion.

The study quality assessment is described below.

Table 2: Inclusion criteria list.

Code Description

IC1 Record written in English

IC2 Record presenting empirical data, theoreti-

cal data, experience reports, or opinions;

IC3 Record published between 10/2019 and

12/2021, inclusive.

IC4 Record is available in its entirety and free

of charge.

IC5 Record of blogs, tweets, annual reports,

news article, presentation, videos, Q/A

sites such as StackOverﬂow, Wiki Arti-

cles, books, magazines, government re-

ports, white papers (Garousi et al., 2019).

Table 3: Exclusion criteria list.

Code Description

EC1 Duplicate records.

EC2 Records not focused on Software Engi-

neering.

EC3 Records unrelated to the Covid-19 pan-

demic.

EC4 Non-English language records.

EC5 Records not responding to RQ’s (RQ1 and

RQ2).

EC6 Records that do not explicitly address in

their reports the impacts of the pandemic

on SE, according to Computing Curricula

2020 - CC2020.

EC7 Records that clearly is only propaganda

of professionals/companies/governments

with the intention of taking advantage of

the pandemic for marketing and selling a

certain product or service.

3.3 Study Quality Assessment

The quality of the records of a GL is measured

through the Tiers or “shades of Grey” (Garousi et al.,

2019; Kamei et al., 2020). These authors state that in

GL reviews it is important to minimally assess the GL

quality. Tiers indicate the quality of the GL in direct

relation to the font type of each record. Its analysis

provides a sort of hierarchy of the possible quality of

the records. The Tiers(Kamei et al., 2020) are:

• Tier-1: books/chapters, white papers, thesis, mag-

azines and government reports;

• Tier-2: annual reports, newspaper articles, Q&A,

company websites;

• Tier-3: blog, social media articles (e.g. tweets).

A Grey Literature Review on the Impacts of Covid-19 in Software Development

201

3.4 Data Extraction

The data extraction and mapping was performed

from a Python scraper algorithm that collected from

Google Search Engine (GSE) the GL records for the

period between October 2019 and December 2021.

The algorithm performs a search on GSE using the

search strings within a window every 24h and not lim-

iting itself to just the ﬁrst search page that displays

only the ﬁrst 10 page rank records, as is usually of-

fered by private web scraping API’s since most free

resources are limited to delivering only the ﬁrst 10

search records. This process generated a corpus with

25,251 records.

3.5 Data Synthesis and Classiﬁcation

The data collected by the scraper was stored in a

*.CSV table. The variables created by the scraper are:

Table 4: Classiﬁcation scheme.

Variable Description

date date of records

header title of records

summary abstract of records by GSE

RQ2 Does que paper answer RQ2?

For the qualitative classiﬁcation, a classiﬁcation

scheme was proposed by “a priori” categories for each

record included in this study, namely: “Analysis of

the adopted work model”, “Work model issues” and

“Ideal working model”, based on the model adopted

by the United Nations (UN, 2019). A template is

available in the Appendix Section. A template is

available in Appendix Section.

4 RESULTS

From an initial set of 25,251 records collected through

the search process, we selected 51 records. The re-

sults are presented below.

4.1 RQ1: How to Automate the

Collection of Evidence from the

Gray Literature to Investigate the

Impacts of Covid-19 in Software

Development?

Automation was performed using a scraper to collect

data from the GL using the Python programming lan-

guage. The scraper made it possible to systematize

data collection (Souza et al., 2021; de Oliveira et al.,

2021). The challenge in automating data collection

is that each site has a unique pattern. As it was not

feasible to create a scraper for each WWW site the

choice was to use the Google Search Engine (GSE).

The script is free and available as an appendix.

4.1.1 Data Extraction Results

The Preferred Reporting Items for Systematic Re-

views and Meta-Analyses (PRISMA) methodology

was used for reporting the systematic review result

and meta-analyses (Page et al., 2021), as shown in

ﬁgure 1. In this diagram ﬂow the screening step was

done in 5 successive statuses (0 to 4) verifying the

relevance of the records to the domain of this study.

Figure 1: Flow diagram, by PRISMA (Page et al., 2021).

In Status1, 11,832 duplicate records were ex-

cluded based on the analysis of the columns “header”,

“summary” and “date”. In Status2, the inclusion (CI)

and exclusion (EC) criteria, cited in table 1, were ap-

plied, resulting in 143 articles considered valid in this

stage. In Status3, the titles, abstracts, and keywords

were read to verify if the record in fact met the study

domain, resulting in 51 selected articles. Finally, in

Status4 these were categorized. Next, we present the

results that help to answer RQ1.

4.1.2 Data Synthesis Results

In addition to the variables planned and mentioned

in Section 3.5, using data science and natural lan-

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guage processing frameworks, it was possible to col-

lect 81 variables in all, the main ones mentioned here:

“domain name”, “ipaddress”, “latitude”, “longitude

”, “city”, “quarter”, “pdf fullText”, “qtd strings”,

“topic dominant”, and “sentiment dominante”.

The data synthesis step was carried out based on a

short protocol was that summarizes the steps and anal-

ysis techniques used in increasing order of complex-

ity. It begins with a temporal visualization by page

rank and day of collection and ends with the applica-

tion of advanced NLP resources, as presented below.

Table 5: Data science, Machine learning and Dataviz Pro-

tocol.

dataviz and machine learning steps

1 Page rank by day of collect data

2 Word cloud

3 Frequency by date

4 Frequency by source

5 Graph analysis of registers

6 Geospatial distribution of the source

7 Sentiment analysis of the total period

8 Sentiment analysis by monthly period

9 Similarity index

10 Q/A with Bert

It is good practice in GL to save eligible records

to “*.pdf” (Garousi et al., 2019; Kamei et al., 2021).

In addition to allowing records to be preserved, we

use python to extract their full text to use as a cor-

pus in the NLP steps of this protocol, for example,

to count the occurrence of keywords and to create a

Word cloud (Sharp et al., 2017; Russell and Norvig,

2009).

Figure 2: Word cloud - Fulltext of records.

This signal cloud was extracted from the full text

of the 51 analyzed records. Next, we present the re-

sults of RQ2.

4.2 RQ2: What Are the Cognitive

Distances in the Social

Representation of the Impacts of

Covid-19 in Software Development?

Cognitive distances resulting from different social

representations or reported perceptions were identi-

ﬁed when analyzing the impact cited in the analyzed

text. For that, we use as reference 3 “a priori” classes:

People, Projects and Organizations. In summary, the

main conclusions about the impacts reported in these

classes are:

• 37 registries reported that Covid-19 had a positive

impact in SE, 25 reported negative impacts and

21 registries reported both positive and negative

impacts.

• Positive impacts on the social representation of

the authors of the records beneﬁt people with 25

records. Organizations were cited in 25 registries

and projects in 15. Some registries cited impacts

in all 3 categories.

• Negative impacts would affect people with 25

records. Organizations with 25 registries and

projects in 15.

The topic modeling analysis revealed that the Peo-

ple, Projects and Organizations classes also emerged

among the 30 Most Relevant Terms for Topic 1

(40.6% of tokens). This unsupervised classiﬁcation

analysis was generated through Latent Dirichlet Al-

location (LDA) and the Sklearn framework (Python)

using the full text of the records. The 4 main topic

clusters are shown in the following ﬁgure.

Figure 3: Topic modeling - LDA.

In the GL Frequency by date analysis, it was found

that there was a higher concentration of records in the

ﬁrst quarter of 2021 and the second quarter of 2020,

respectively.

It was in the third quarter of 2020 that the ﬁrst

mention of the “work from home” phenomenon was

A Grey Literature Review on the Impacts of Covid-19 in Software Development

203

Temporal distribution by quarter

Figure 4.

related to the hybrid trend, as shown by the following

evidence:

• knowledgehut.com (Knowledgehut, 2022), Ev-

idence: “Following the pandemic, working from

home more frequently (perhaps 2-3 days per

week) may become an accepted norm for many

companies, as this could realize cost efﬁciencies

and prove that an agile, remote working model is

productive.” Date: 2020-07-24.

This process was intensiﬁed in the ﬁrst quarter of

2021. In addition, other sub-questions helped to an-

swer RQ2, facilitating the analysis of social represen-

tations reported through the following previous ques-

tions, as shown in Appendix.

The questions about the adopted work model

and the ideal work model were classiﬁed (Microsoft,

2021b). Figure 5 shows the summary of the results.’

Figure 5: Social representation - RQ2.

The remaining questions were categorized be-

tween “Yes” and “No”. When a record did not ad-

dress the question, this record was categorized as NA.

The main results are presented below.

4.2.1 Work Model Adopted

Because of the current pandemic situation, the ﬁrst

question, “Check the option that best represents the

work model adopted by the company” was catego-

rized in all 51 records as ”fully remot”. This was the

only option identiﬁed in the reports during this study.

4.2.2 Work Model Issues

A couple of questions were proposed that could in-

dicate the status of the current and future scenario of

Covid-19 impacts on the SE. One of the questions was

to see if there are any reports of negative impacts.

Figure 6: The pandemic had a negative impact on the

change in the work model.

The reported social representations indicate that

38 records (or 74.50%) do not consider that Covid-19

has negatively impacted the work model. Followed

by 18 records (or 35.29%) who afﬁrm that there is

some negative impact. Only 1 record did not report

this issue.

When analyzing the impacts of engagement of the

teams with the company, according to ﬁgure 7, it was

found that 12 records (or 23.52%) believe that there

are negative impacts of Covid-19. Only 5 records (or

0.98%) afﬁrm that there are no negative impacts, and

34 records did not report this topic.

Figure 7: The work model adopted has a negative impact on

the team’s engagement with the company.

Another relevant question was to verify if ”The

current working model is different from the dis-

tributed software development (DDS) model”, ac-

cording to 8. In this sense, it was veriﬁed that 15

records (or 29.41%) indicate that there are differences

between the current remote working model and the

model traditionally known as DDS. Followed by 12

Figure 8: The current working model is different from the

distributed software development (DDS) model.

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records (or 23.52%) do not consider there are differ-

ences between the current model and DDS. And 24

records did not mention this topic.

4.2.3 Ideal Work Model

Another aspect worth mentioning is “Check the op-

tion that best represents the ideal work model”, ac-

cording to 9. In this sense, we veriﬁed that 22 records

(or 43.13%) already indicated the hybrid model with

ﬂexibility as ideal. Followed by 10 records (or

19.60%) indicating the totally remote model. Only 1

record reported the hybrid without ﬂexibility as ideal

and 18 records did not report this topic.

Figure 9: Check the option that best represents the ideal

work model.

These were the main results achieved in RQ2. Fol-

lowing are the discussions.

5 DISCUSSION

This research was an important opportunity to tran-

sit through interdisciplinarity, necessary and desired

in the analysis of any complex problem. Here con-

cepts from Software Engineering, Social Representa-

tion, and Sociology were brought together to contex-

tualize the very notion of what “work” is.

The common thread that made it possible to link

all these different ﬁelds of knowledge was the system-

atic review of the Grey Literature. The results of this

union generated as a beneﬁt a methodological pro-

posal for the automation of the Grey Literature data

collection. Moreover, to guide the analytical process,

a small protocol was proposed that contains the main

steps and techniques often used in text data synthesis,

especially when adopting natural language processing

(NLP) resources.

The distributions of social representations identi-

ﬁed in the observed reports indicate that there is con-

siderable cognitive distance as to the ways in which

the Covid-19 pandemic has impacted the SE work-

ing model. At some points, there appears to be

agreement, but not without distinct points of view.

Still, when analyzing the trend of impact on the work

model, one point stood out: the ”hybrid model,” be-

ing reported in 23 records (or 45.09%), considering

the possibility of a ”hybrid model” of work with or

without ﬂexibility.

This ﬁnding reinforces what researchers in Soci-

ology such as Domenico de Masi and in Computing

such as Silvio Meira have been saying for some time

(de Masi, 2021; Meira, 2021). First, more than 40

years ago Domenico de Masi asserted the possibil-

ity of remote work. Second, Silvio Meira states that

the future is neither totally face-to-face nor totally re-

mote. The author suggests that the future is Phygital,

an acronym resulting from 3 dimensions that are the

Physical, Digital, and Social dimensions, according

to Silvio Meira.

Invariably, whatever the shape of this future of

work in software engineering, something new seems

to have awakened. This apparent newness is summed

up in the desire for ﬂexibility. Everything indicates

that it is the notion of this concept and the evolution of

this ﬂexibility in the mindset of developers and com-

panies in the software industry that may bring new

perspectives and possibilities that will shape the true

future of software engineers’ work.

6 CONCLUSIONS

The results revealed that at GL there are distant views

on the impacts of Covid-19 on software development.

The pandemic triggered secondary phenomena that

were called, for example, “remote work”, “work from

home” and ﬂexible work”.

These terms are the result of different social repre-

sentations of the same macro-event, which is the very

impact of Covid-19 on the work model. As future

work, further research is suggested on the most recent

of these secondary phenomena: the “hybrid” model.

Questions such as: What is being a hybrid and

what does it imply in the practice of higher education?

What would be the guidelines or best practices for a

hybrid model to work? These are just a few questions

that can help professionals and companies create new

disaster risk reduction protocols aimed at building re-

silience and adaptation in combating the impacts of

Covid-19 or future disasters that may impact software

development.

ACKNOWLEDGEMENTS

Rafael Prikladnicki is partially funded by Fapergs and

CNPQ.

A Grey Literature Review on the Impacts of Covid-19 in Software Development

205

REFERENCES

Bogoch, I., Watts, A., Thomas-Bachli, A., Huber, C., Krae-

mer, M., and Khan, K. (2020). Pneumonia of un-

known etiology in wuhan, china: Potential for inter-

national spread via commercial air travel. Journal of

travel medicine, 27.

de Masi, D. (2021). Jota – domenico de masi: “voc

es,

brasileiros, podem ser um modelo para o mundo”.

de Oliveira, P. A. M., de Alc

antara dos Santos Neto, P.,

de Andrade e Silva Silva, G., Ibiapina, I., Lira, W.,

and de Castro Andrade, R. M. (2021). Software de-

velopment during COVID-19 pandemic: an analysis

of stack overﬂow and github. CoRR, abs/2103.05494.

Garousi, V., Felderer, M., and M

antyl

a, M. V. (2019).

Guidelines for including grey literature and conduct-

ing multivocal literature reviews in software engineer-

ing. Information and Software Technology, 106:101–

121.

Jodelet, D. (2001). As representac¸

oes sociais. EdUERJ, Rio

de Janeiro, Brazil.

Kamei, F., Wiese, I., Lima, C., Polato, I., Nepomuceno, V.,

Ferreira, W., Ribeiro, M., Pena, C., Cartaxo, B., Pinto,

G., and Soares, S. (2021). Grey literature in software

engineering: A critical review. Information and Soft-

ware Technology, 138:106609.

Kamei, F., Wiese, I., Pinto, G., Ribeiro, M., and Soares, S.

(2020). On the use of grey literature: A survey with

the brazilian software engineering research commu-

nity. In Proceedings of the 34th Brazilian Symposium

on Software Engineering, SBES ’20, page 183–192,

New York, NY, USA. Association for Computing Ma-

chinery.

Kitchenham, B. and Charters, S. (2007). Guidelines for per-

forming systematic literature reviews in software en-

gineering.

Knowledgehut (2022). Key insights from the 2020 state of

agile report.

Meira, S. (2021). Desnudando o telerabalho: transformac¸o

digital, o teletrabalho e as perspectivas para o futuro.

Microsoft (2021a). New future of work.

Microsoft (2021b). The new future of work:research from

microsoft into the pandemic’s impact on work prac-

tices.

Moscovici, S. (2007). Representac¸

oes sociais:

investigac¸

oes em psicologia social, 5ª ed. Vozes,

Petr

opolis, Brazil.

Nooteboom, B., Van Haverbeke, W., Duysters, G., Gils-

ing, V., and van den Oord, A. (2007). Optimal cogni-

tive distance and absorptive capacity. Research Policy,

36(7):1016–1034.

Oliveira, E., Leal, G., Valente, M. T., Morandini, M., Prik-

ladnicki, R., Pompermaier, L., Chanin, R., Caldeira,

C., Machado, L., and de Souza, C. (2020). Surveying

the impacts of covid-19 on the perceived productivity

of brazilian software developers. In Proceedings of

the 34th Brazilian Symposium on Software Engineer-

ing, SBES ’20, page 586–595, New York, NY, USA.

Association for Computing Machinery.

Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoff-

mann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff,

J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville,

J., Grimshaw, J. M., Hr

objartsson, A., Lalu, M. M.,

Li, T., Loder, E. W., Mayo-Wilson, E., McDonald,

S., McGuinness, L. A., Stewart, L. A., Thomas, J.,

Tricco, A. C., Welch, V. A., Whiting, P., and McKen-

zie, J. E. (2021). Prisma 2020 explanation and elabo-

ration: updated guidance and exemplars for reporting

systematic reviews. BMJ, 372.

Prikladnicki, R. (2003). Munddos : um modelo de re-

fer

encia para desenvolvimento distribu

ıdo de soft-

ware. Master’s thesis, Escola Polit

ecnica, Programa

de P

os-Graduac¸

ao em Ci

encia da Computac¸

ao.

Ralph, P., Baltes, S., Adisaputri, G., Torkar, R., Kovalenko,

V., Kalinowski, M., Novielli, N., Yoo, S., Devroey, X.,

Tan, X., Turhan, B., Hoda, R., Hata, H., Robles, G.,

Milani Fard, A., and Alkadhi, R. (2020). Pandemic

programming: How covid-19 affects software devel-

opers and how their organizations can help. Empirical

software engineering, 25:1–35.

Russell, S. J. and Norvig, P. (2009). Artiﬁcial Intelligence:

a modern approach. Pearson, 3 edition.

Sharp, B., Sedes, F., and Lubaszewski, W. (2017). Cogni-

tive Approach to Natural Language Processing. ISTE

Press - Elsevier, London/Oxford, GBR, 1st edition.

Souza, W. M., Filho, W., and Santos, W. (2021). Ferramenta

de web-scraping: Impactos da covid-19 na ind

ustria

de software. In Anais Estendidos do XVII Simp

osio

Brasileiro de Sistemas de Informac¸

ao, pages 49–52,

Porto Alegre, RS, Brasil. SBC.

UN (’2019’). Flexible working arrangements

(st/sgb/2019/3).

APPENDIX

Here is a link to access the graphs and tables of the

data analyzed in this study: https://bit.ly/34k2t3m

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