3 |  How to Use |  More than 50% of the questions are How-type, showing the needs for better learning resources. |  The official documentation of implementation can be further enhanced by processing the crowd-shared knowledge of the usage discussions of the platforms (e.g., from Stack Overflow)
|  Server Setup |  What and Why-type questions are most dominant for server setup related. |  LCSD platform should provide provide better visualizer and debugger for the practitioners to improve troubleshooting.
4 |  Development Effort |  Implementation is the most dominant SDLC phase (65%). Interestingly we also around one third of the are related to Application Design (17%) and Requirement Analysis & Planning (9%) |  LCSD platform providers should take necessary steps to provide better community support in SO to address these challenges. Practitioners should also be aware LCSD platforms can improve the development of traditional software development team but they are not yet panacea for all problems.
5 |  Deployment vs Maintenance |  Questions related to both the deployment-SDLC and Maintenance-SDLC as the most popular and hardest to get accepted answers. |  Platform providers should provide better level of abstraction for cloud management, application deployment and monitoring. Educators can provide better resources to learn about cloud platforms.
|  Messaging |  The topic message queue from platform adoption is the most difficult to get an accepted answer. This topic contains discussion about the adoption of a general micro-service architecture within LCSD platforms |  LCSD platforms can improve the adoption of micro-service architecture and the communication between different the microservices with better message queuing

###  5.6 Implications

In Table 7, we summarize the core findings of our study and provide
recommendations for each findings. The findings from our study can guide the
following three stakeholders:  (1) LCSD platform Providers to improve the
documentation, deployment, and maintenance support,  (2) LCSD
Practitioners/Developers to gain a better understanding of the trade-offs
between rapid development and customization constraints,  (3) Community of
LCSD Researchers & Educators to have a deeper understanding of the significant
challenges facing the broader research area to make software development more
accessible.  We discuss the implications below.

In this empirical study, we infer implications and recommendations based on
our observation of practitioners’ discussion in SO. So further validation from
the developers’ survey can provide more insight. However, the diversity of the
low-code platforms and topics makes it non-trivial to design a proper survey
with representative sample of LCSD practitioners. Therefore, the findings can
be used to design multiple LCSD related surveys focusing on different low-code
topics and platforms.

LCSD Platform Vendors. In order to better understand the issues of LCSD, we
present a bubble chart with difficulty and popularity of different aspects of
LCSD such as Topic Category in Figure 11, Types of questions in Figure 19 and
agile SDLC phases in Figure 18. These findings coupled with the evolution of
LCSD platforms (14) and discussions (7) shows that Customization and Data
Storage related queries are more prevalent, with the majority of these queries
occurring during Implementation agile SDLC stage. However, one of our
interesting findings is Platform Adoption related queries are increasing in
popularity. LCSD practitioners find LCSD platform infrastructure and server
configuration-related quires tough and popular during the Deployment and
Maintenance phase. The top five most challenging topics belong to Platform
Adoption and Maintenance topic category.

Many new practitioners make queries regarding LCSD platforms, learning
resources, basic application and UI customization, and how to get started with
this new emerging technology. Figure 17 shows that Platform Related Query
topic is the most popular among LCSD practitioners. We find that Documentation
related queries are both top-rated and challenging. Our findings also suggest
that many practitioners still face challenges during testing, especially with
third-party testing tools like JUnit (in $Q_{9811992}$) and troubleshooting.
Consequently, many of the questions on this topic remain unanswered. It
reveals that to ensure smooth adoption of the LCSD platforms, providers should
provide better and more effective documentation and learning resources to
reduce entry-level barriers and smooth out the learning curve.

LCSD Practitioners/Developers. Gartner gartner estimates that by 2022, more
than half of the organizations will adapt LCSD to some extent. Additionally,
our analysis reveals a rising trend for LCSD approaches, particularly during
Covid-19 pandemic (Fig. 6). We can also see that new LCSD platforms such as
Microsoft Powerapps are gaining many developers’ attention. LCSD platform
enables practitioners with diverse experience to contribute to the development
process even without a software development background. However, our finding
shows that practitioners find debugging, application accessibility, and
documentation challenges. Hence, the practitioners should take the necessary
steps to understand the tradeoffs of LCSD platforms’ features deeply. The
project manager should adopt specific strategies to customize, debug, and test
the application. For example, many practitioners struggle with general Third-
Party API integration and database design and query. We find that DevOps-
related tasks such as CI/CD, Server configuration, and monitoring-related
queries are most challenging to the practitioners. So, a well-functioning LCSD
team should allocate time and resources to them. It provides valuable insights
for project managers to manage resources better (i.e., human resources and
development time).

Figure 18: The popularity vs. difficulty bubble chart for low-code software
development life cycle phases

Figure 18 shows that Maintenance is the most popular development phase,
followed by Deployment, and Testing is the least popular SDLC phase.
Similarly, the figure also shows that questions asked in deployment phase are
the most difficult followed by Maintenance. Implementation, Requirement
analysis and planning, Application design phase are in the middle range in
terms of popularity and difficulty spectrum. Thus, our analysis indicates that
LCSD practitioners face more broad and complex application maintenance and
deployment-related challenges, on which LCSD platform vendors should
concentrate their efforts. This finding can influence the decision-making
process of LCSD developers and practitioners like prioritizing their efforts
during the design, development, and deployment of software that uses LCSD
platforms. For example, if sufficient support or tools are not available for
scalable usage and deployment of an LCSD platform, developers may look for
alternatives that have better deployment and maintenance support.

One fundamental shortcoming of LCSD platforms is that their abstraction and
feature limitations can make customization and debugging extremely difficult.
Additionally, managed cloud platforms make data management and deployability
more challenging sahay2020supporting ; luo2021characteristics . The findings
in this study help to present some strengths and limitations of the overall
LCSD paradigm, which complements the findings of other studies
sahay2020supporting ; alsaadi2021factors ; luo2021characteristics ;
waszkowski2019low-automating ; adrian2020app ; ness2019potential . The
analysis could assist LCSD teams in selecting the appropriate LCSD platforms,
which is critical for future success.

LCSD Researchers & Educators. The findings of this study have many
implications for researchers and educators of LCSD platforms and the border
research community to improve the software development process. We discover
that What-type and How-type questions are popular among LCSD practitioners.
They also find them challenging because of adequate usable documentation.
Thus, practitioners ask questions about certain limits or how to implement
certain features, and in the accepted answer, some other user simply points to
the official documentation page (e.g., “Domino Data Service API Documentation”
in $Q_{59739877}$ and $Q_{5806293}$). Many of the challenges faced by low-code
petitioners are similar to traditional software developers. So, researchers
from border software engineering domain can contribute to improving aspects
such as improving documentation bayer2006view ; khan2021automatic ;
bhat2006overcoming , improving API description usage uddin2015api ;
uddin2021automatic and make it more accessible to general practitioners. In
the Customization and Data Storage topic category, we find practitioners
asking help in generic programming queries, database design, and file
management. So, research on those topics will also help the adoption of LCSD.
Some LCSD platforms provide great in-build support for unit and functional
testing. However, we find around 2.1% of questions belong to Testing topic.
Most of these LCSD platforms heavily rely on cloud computing, and thus
research improvement of server configuration and library management, i.e.,
DevOps zhu2016devops in general, will aid in better platforms. On the other
hand, educators can focus their efforts on making the learning resources on
Automatic testing, Server config. and DevOps practices such as CI/CD more
accessible to the citizen developers.

Figure 19: The popularity vs. difficulty of different types of LCSD-related
questions

Figure 19 shows What-type of posts are most popular, followed by Why-type,
How-type, and Others-type. Additionally, it demonstrates that the most
challenging question type is Why-type, followed by What-type, How-type, and
others. So, although How-type questions are most dominant, the details types
(i.e., Why-type, What-type) of questions are more popular and challenging.
This analysis implies that LCSD practitioners have a harder time finding
detailed information regarding different platform features. As a result, LCSD
platform providers should improve their documentation. Intuitively, How-type
questions can be answered with better documentation for LCSD platforms. Given
the official API documentation can often be incomplete and obsolete Uddin-
HowAPIDocumentationFails-IEEESW2015 ; Khan-DocSmell-SANER2021 and given that
our research shows that LCSD developers use SO to ask questions about various
topics, LCSD researchers can develop techniques and tools to automatically
improve the documentation of LCSD platforms by analyzing SO questions and
answers. Indeed, existing research efforts show that solutions posted in SO
can be used to improve API documentation by supporting diverse development
tasks and programming languages Chakraborty-NewLangSupportSO-IST2021 ; Uddin-
OpinerAPIUsageScenario-IST2020 ; Uddin-OpinerAPIUsageScenario-TOSEM2020 ;
Uddin-OpinerReviewAlgo-ASE2017 ; Uddin-OpinionSurvey-TSE2019 ; Uddin-
OpinionValue-TSE2019 ; Uddin-OpinerReviewToolDemo-ASE2017 .

We find that the LCSD paradigm’s challenges can be different from traditional
software development sahay2020supporting . Simultaneously, researchers can
study how to provide better tools for practitioners to customize the
application. Security is an open research opportunity for such platforms as a
security vulnerability in such platforms or frameworks could compromise
millions of applications and users lin2020software . Researchers can develop
better testing approaches to ensure faster development and dependability.
Educators can also benefit from the results presented in Table 4, 4 and Figure
17 to prioritize their focus on different topics such as Library Dependency
Mngmt, Web-Service Communication, Asynchronous Batch Jobs, Testing, Dynamic
Form Controller.

##  6 Threats to Validity

Internal validity threats, in our study, relate to the authors’ bias while
conducting the analysis as we have manually labeled the topics. We mitigate
the bias in our manual labeling of topics, types of questions, and LCSD phases
by consulting the labels among multiple authors and resolving any conflicts
via discussion. Four of the authors actively participated in the labelling
process. The first author reviewed the final labels and refined the labels by
consulting with the second author.

Construct Validity threats relate to the errors that may occur in data
collection, like identifying relevant LCSD tags. To mitigate this, we created
our initial list of tags, as stated in Section 3, by analyzing the posts in SO
related to the leading LCSD platforms. Then we expanded our tag list using
state-of-art approach bagherzadeh2019going ; abdellatif2020challenges ;
ahmed2018concurrency ; rosen2016mobile . Another potential threat is the topic
modeling technique, where we choose $K$ = 45 as the optimal number of topics
for our dataset $B$. This optimal number of topics has a direct impact on the
output of LDA. We experimented with different values of $K$ following related
works abdellatif2020challenges ; bagherzadeh2019going . We used the coherence
score and manual examination to find $K$’s optimal value that gives us the
most relevant and generalized low-code related topics alamin2021empirical ;
iot21 ; abdellatif2020challenges .

External Validity threats relate to the generalizability of our findings. Our
study is based on data from developers’ discussions on SO. However, there are
other forums LCSD developers may use to discuss. We only considered questions
and accepted answers in our topic modeling. We also had the option of choosing
the best answer. In SO, the accepted answer and best answer may be different.
Accepted answer is the one approved by the questioner while the best answer is
voted by all the viewers. as discussed in Section 5.1 it is quite difficult to
detect if an answer is relevant to the question or not. Thus We chose the
accepted answer in this study because we believe that the questioner is the
best judge of whether the answer solves the problem or not. Even without the
unaccepted answers, our dataset contains around 38K posts (27K questions + 11K
accepted answers). This also conforms with previous works iot21 ;
abdellatif2020challenges ; alamin2021empirical ; yang2016query . Some novice
practitioners post duplicate questions, assign incorrect tags, and provide
inadequate descriptions, which receives an overall negative score from the
community. To ensure that topics contain relevant discussion of high quality,
we only use posts with non-negative scores. Nevertheless, we believe using
SO’s data provides us with generalizability because SO is a widely used Q&A
platform for developers. However, we also believe this study can be
complemented by including the best answers to the questions in SO, as we
discussed earlier, including discussions from other forums, surveying, and
interviewing low-code developers.

##  7 Related Work

We previously published a paper at the MSR 2021 based on an empirical study of
LCSD topics in SO (see alamin2021empirical ). We compared the findings of this
paper against our previous paper in Section 1. Other related work can broadly
be divided into two categories: SE (Software Engineering) research on/using
(1) low code software development (Section 7.1), and  (2) topic modeling
(Section 7.2).

###  7.1 Research on Low Code Software Development and Methodologies

LCSD is a new technology, with only a handful of research papers published in
this field. Some research has been conducted on the potential applications of
this developing technology in various software applications lowcodeapp or for
automating business process in manufacturing waszkowski2019low-automating ,
healthcare ness2019potential ; woo2020rise , Digital transformation
phalake2021low , Industrial engineering educationadrian2020app , IoT systems
using LCSD ihirwe2020low . Sipio et al. di2020democratizing present the
benefits and future potential of LCSD by sharing their experience of building
a custom recommendation system in the LCSD platform. Kourouklidis et al.
kourouklidis2020towards discuss the low-code solution to monitor the machine
learning model’s performance. Sahay et al. sahay2020supporting survey LCDP and
compare different LCDPs based on their helpful features and functionalities.
Khorram et al. lowcodetesting analyse commercial LCSD platforms and present a
list of features and testing challenges. Zhuang et al. zhuang2021easyfl
created a low-code platform called EasyFL where researchers and educators can
easily build systems for privacy-preserving distributed learning method.
Ihirwe et al. lowcodeIot analyse 16 LCSD platforms and identifies what IoT
application-related features and services each platform provides. All of these
studies compare a single LCSD platform and its support and limitations for
various sorts of applications alonso2020towards , rather than taking a
holistic picture of the difficulties that the broader community faces.

There are also some studies where researchers proposed different techniques to
improve LCSD platform such as Overeem et al. overeem2021proposing on LCSD
platform’s impact analysis, Jacinto et al. jacinto2020test improve testing for
LCSD platforms.

Additionally, there are some studies that describe the difficulties faced by
LCSD practitioners. The main research methodology and objectives of these
studies, however, are significantly different from this study. Yajing et al.
luo2021characteristics analyse the LCSD platform’s characteristics including
programming languages used, major implementation units, supporting
technologies, applications being developed, domains, etc., along with the
benefits, limitations, and challenges by collecting relevant posts from SO and
Reddit. In this study, we use tag-based approach to find relevant LCSD-related
posts which is much more reliable than text-based searching. Furthermore, the
SO related discussion used in this study is significantly larger and our
research objective about LCSD platforms challenges are quite different.
Timothy et al. lethbridge2021low discuss experiences with several low-code
platforms and provide recommendations focusing on low-code platforms enabling
scaling, understandability, documentability, testability, vendor-independence,
and the overall user experience for developers as end-users who do some
development. Danial et al. dahlberg2020developer and ALSAADI et al.
alsaadi2021factors Surveyed on factors hindering the widespread adaptation of
LCSD by interviewing LCSD developers or conducting a survey. To the best of
our knowledge, ours is the first empirical study of LCSD platforms based on
developers’ discussions from Stack Overflow, and hence our findings complement
those of other studies.

###  7.2 Topic Modeling in Software Engineering

Our motivation to use topic modeling to understand LCSD discussions stems from
existing research in software engineering that shows that topics generated
from textual contents can be a good approximation of the underlying themes
Chen-SurveyTopicInSE-EMSE2016 ; Sun-SoftwareMaintenanceHistoryTopic-CIS2015 ;
Sun-ExploreTopicModelSurvey-SNPD2016 . Topic models are used recently to
understand software logging Li-StudySoftwareLoggingUsingTopic-EMSE2018 and
previously for diverse other tasks, such as concept and feature location
Cleary-ConceptLocationTopic-EMSE2009 ; Poshyvanyk-FeatureLocationTopic-TSE2007
, traceability linking (e.g., bug) Rao-TraceabilityBugTopic-MSR2011 ;
asuncion2010software , to understand software and source code history
evolution Hu-EvolutionDynamicTopic-SANER2015 ; Thomas-
SoftwareEvolutionUsingTopic-SCP2014 ; Thomas-EvolutionSourceCodeHistoryTopic-
MSR2011 , to facilitate code search by categorizing software Tian-
SoftwareCategorizeTopic-MSR2009 , to refactor software code base Bavota-
RefactoringTopic-TSE2014 , as well as to explain software defect Chen-
SoftwareDefectTopic-MSR2012 , and various software maintenance tasks Sun-
SoftwareMaintenanceTopic-IST2015 ; Sun-SoftwareMaintenanceHistoryTopic-CIS2015
. The SO posts are subject to several studies on various aspects of software
development using topic modeling, such as what developers are discussing in
general barua2014developers or about a particular aspect, e.g., concurrency
ahmed2018concurrency , big data bagherzadeh2019going , chatbot
abdellatif2020challenges .

Table 8: Comparing the popularity and difficulty metrics of different domains

Type | Metrics | LCSD | IoT | Big Data | Chatbot | Security | Mobile | Concurrency
---|---|---|---|---|---|---|---|---
P | # Posts | 33,766 | 53,173 | 125,671 | 3,890 | 94,541 | 1,604,483 | 245,541
Avg View | 1330.6 | 1,320.3 | 1,560.4 | 512.4 | 2,461.1 | 2,300.0 | 1,641
Avg Favorite | 1.2 | 1.5 | 1.9 | 1.6 | 3.8 | 2.8 | 0.8
Avg Score | 0.7 | 0.8 | 1.4 | 0.7 | 2.7 | 2.1 | 2.5
D | % W/o Acc. Ans | 41% | 64% | 60.3% | 67.7% | 48.2% | 52% | 43.8%
Med Hrs to Acc. | 5.7 | 2.9 | 3.3 | 14.8 | 0.9 | 0.7 | 0.7

Table 9: Comparative analysis of question types across different domains Question Type | How | What | Why | Others
---|---|---|---|---
LCSD | 55.6% | 17.9% | 14% | 12.5%
IoT | 47.3% | 37.9% | 20% | 8.3%
Chatbot | 61.8% | 11.7% | 25.4% | 1.2%

In particular, SO posts have been used in various studies where the
researchers analysed topics for that particular domains. For instance, SO
posts has been used to study developers challenges in IoT iot21 , big data
bagherzadeh2019going , chatbots abdellatif2020challenges and so on. The
distributions and the nature of these posts differs. As SO is arguably the
most popular public forum for developers, the analysis of these domains’
characteristics may help us identify the SO community better. Therefore, a
systematic analysis of these domains is interesting. Following related
studiesiot21 , we use six metrics in this study:  (1) Total #posts,  (2) Avg
views,  (3) Avg favorite,  (4) Avg score,  (5) Percentage of questions without
accepted answers,  (6) Median hours to get accepted answers per domain.  The
first four metrics are popularity metrics and the last two are difficulty
metrics.

In this study, we do not replicate the findings of the original study in our
dataset. Rather we only report the findings from the original study. So
following related work iot21 , we compared our LCSD-related discussions with
other five domains: IoT iot21 ,big databagherzadeh2019going , security
yang2016security , mobile apps Rosen-MobileDeveloperSO-ESE2015 , chatbots
abdellatif2020challenges and concurrency ahmed2018concurrency .

Table 8 provides an overview of the seven metrics. We can see that it has a
greater number of SO posts than chatbot domains but fewer than the other five
domains. There are two other studies on Blockchain wan2019discussed and deep
learning han2020programmers where the total number of posts are 32,375 and
25,887, respectively. However, these two studies did not report the other
metrics, so they are excluded from the Table. Although the LCSD-related
discussion may have fewer posts than these other domains, as discussed in RQ3,
this number is increasing rapidly.

We can also observe that the LCSD domain shows similarities with IoT,
Concurrency domain in terms of Avg. View count. Security and Mobile domain
seem most popular in terms of Avg. Favourite count and LCSD rank lower in this
metric. LCSD domains most resemble with IoT domain in terms of Avg. View, Avg.
Favourite and Avg. Score. In terms of difficulty metrics percentage of posts
without accepted answers, LCSD domain ranks lowest, which is good. However, it
takes much longer to get accepted answers( 0.7 hours for Mobile, 2.9 for IoT).
Only the chatbot domain requires more time to get accepted answers (i.e., 5.3
hours in LCSD vs 14.8 hours in chatbot).

We further discuss the LCSD, IoT, and Chatbot domains with the distribution of
different types of questions in Table 9. We find that LCSD domain is in the
middle of IoT and Chatbot domains in terms of How-type (57%) compared to IoT
(47%) and chatbot (62%). This signifies that LCSD domain practitioners ask
more about implementation-related questions. In the Why-type question
percentage of LCSD-related domains is lowest (14%) compared with IoT (20%) and
chatbot (25%). This suggests that practitioners in the LCSD domain enquire
about relatively modest troubleshooting issues. For What-type, we notice that
the IoT domain dominates with 38% of questions, compared to the LCSD domain’s
12%. Practitioners of LCSD are less inquisitive about domain architecture and
technologies compared to IoT domain. As a result of these analyses, we can see
that LCSD domain practitioners exhibit several traits that distinguish them
from practitioners in other domains, which LCSD vendors and educators should
take into account.

##  8 Conclusions

Low Code Software Development (LCSD) is a novel paradigm for developing
software applications utilizing visual programming with minimum hand-coding.
We present an empirical study that provides insights into the types of
discussions low-code developers discuss in Stack Overflow (SO). We find 40
low-code topics in our dataset of 33.7K SO posts (question + accepted
answers). We collected these posts based on 64 SO tags belonging to the
popular 38 LCSD platforms. We categorize them into five high-level groups,
namely Application Customization (30% Questions, 11 Topics), Data Storage (25%
Questions, 9 Topics), Platform Adoption (20% Questions, 9 Topics), Platform
Maintenance (14% Questions, 6 Topics), and Third-Party Integration (12%
Questions, 5 Topics). We find that the Platform Adoption topic category has
gained popularity recently. Platform Related Query and Message Queue topics
from this category are the most popular. On the other hand, We also find that
practitioners find Platform Adoption and Maintenance related topics most
challenging. How-type questions are the most common, but our research reveals
that practitioners find what-type and why-type questions more difficult.
Despite extensive support for testing, deployment, and maintenance, our
analysis shows that server configuration, data migration, and system module
upgrading-related queries are widespread and complex to LCSD practitioners.
Despite significant testing, deployment, and maintenance support, our analysis
finds numerous and complex queries regarding server configuration, data
migration, and system module updating. Our analysis finds that better
tutorial-based documentation can help solve many of these problems. We also
find that during the Covid-19 pandemic, LCSD platforms were very popular with
developers, especially when it came to dynamic form-based applications. We
hope that all of these findings will help various LCSD stakeholders (e.g.,
LCSD platform vendors, practitioners, SE researchers) to take necessary
actions to address the various LCSD challenges. Since the growth indicates
that this technology is likely to be widely adopted by various companies for
their internal and customer-facing applications, platform providers should
address the prevailing developers’ challenges. Our future work will focus on
(1) getting developers’ feedback on our study findings based on surveys and
developer interviews, and  (2) developing tools and techniques to
automatically address the challenges in the LCSD platforms that we observed.

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