=Paper=
{{Paper
|id=Vol-3272/paper10
|storemode=property
|title=Tools/Frameworks that Support Development Process of AI-based Software: Validations in White Literature
|pdfUrl=https://ceur-ws.org/Vol-3272/IWSM-MENSURA22_paper10.pdf
|volume=Vol-3272
|authors=Tugba Gurgen Erdogan,Haluk Altunel,Ayca Kolukısa Tarhan
|dblpUrl=https://dblp.org/rec/conf/iwsm/ErdoganAT22
}}
==Tools/Frameworks that Support Development Process of AI-based Software: Validations in White Literature==
https://ceur-ws.org/Vol-3272/IWSM-MENSURA22_paper10.pdf
Tools/frameworks that support development process
of AI-based software: validations in white literature
Tugba Gurgen Erdogan1,* , Haluk Altunel2,3 and Ayca Kolukısa Tarhan1
1
Computer Engineering Department of Hacettepe University, Beytepe Campus, Ankara, 06800, Turkey
2
Computer Engineering Department of Bilkent University, Bilkent, 06800, Turkey
3
Softtech Inc Hacettepe Teknokent, 6.arge, Beytepe, Ankara, 06800, Turkey
Abstract
Context: Artificial Intelligence (AI)-based software has gained increasing interest, especially in the last
decade, due to advancements in underlying technologies and demands in varying business domains. With
the proliferation to develop such software, there appears a need for developing methods and supporting
tools/frameworks. Purpose: In this paper, we focus on tools/frameworks to automate AI-based software
development process, from a holistic view. We review the scientific studies that were empirically validated
and also evaluate their proposals with respect to basic characteristics including theme, research methods,
types, domains, and a number of cases in empirical validations. Method: We elicit relevant studies (with
the contribution type of tool or framework) from a larger set of primary studies identified by a systematic
literature review on AI-based software development process. We select 14 primary studies in this context
and analyze them with respect to the purposes of the proposals. Results: We review tools/frameworks
that support AI-based software development process under four headings: software system development
process, the development process of fair software, model development process, and model deployment
and operation processes. We observe that domains of empirical validation are diverse while the number
of empirical cases applied for validation is limited. Also, only half of the primary studies provide links to
their proposals as open-source, which is very important for the repeatability of the empirical validations.
Keywords
Tool, framework, artificial intelligence, machine learning, software development, development process
1. Introduction
Almost every day, we hear about intelligent systems such as self-driving cars and unmanned
aerial vehicles that come to life with artificial intelligence. In addition and in parallel to the era
of digitalization, the demand for smart systems that analyze existing data and turn it into an
advantage for institutions and customers in many business areas such as trade, health, finance,
production, education, etc. is increasing. Accordingly, the growing scale of Internet-based
systems and innovations in social platforms, autonomous systems and cloud computing, and
IWSM-MENSURA’22: The Joint Conference of the 31th International Workshop on Software Measurement (IWSM) and
the 16th International Conference on Software Process and Product Measurement (MENSURA) Sep 28–30, 2022, Izmir,
Turkey
*
Corresponding author.
$ tugba@cs.hacettepe.edu.tr (T. G. Erdogan); altunel@bilkent.edu.tr (H. Altunel); atarhan@cs.hacettepe.edu.tr
(A. K. Tarhan)
0000-0003-1491-8739 (T. G. Erdogan); 0000-0003-1103-3644 (H. Altunel); 0000-0003-1466-9605 (A. K. Tarhan)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
the advancement of artificial intelligence (AI) and machine learning (ML) techniques in the
last decade, have created a new specialty for the software industry: AI-based software system
development.
Several studies have reported challenges about AI-based software development, more fre-
quently for ML software [1, 2, 3] or deep learning (DL) software [4], and more specifically for
development phases such as requirements [5] or testing [6]. In addition, large companies such
as Google and Microsoft have shared their experiences of AI-based (or data-centric) software
development in recent years. For example, Google reported that machine learning software used
in everyday life will likely require ongoing maintenance costs [7]. Microsoft reported about the
challenges AI teams face in managing data, component abstraction, and reusing software, noting
that customizing and reusing AI-based software requires different methods and skills than those
applied in traditional software development [8]. Consequently, development methods and
supporting technologies must be adapted to meet the specific challenges of AI-based software
engineering.
As a response to the need stated above, in this paper, we focus on and review in detail the
studies in scientific (or white) literature, which propose the tools/frameworks to support the
development process of AI-based software. We select 14 studies that have been empirically
validated from a larger set of primary studies identified by a systematic literature review (SLR)
on AI-based software development process. We review the studies by grouping them according
to their purposes of proposals and evaluate their proposals with respect to basic characteristics
including theme, research methods and types, domains and number of cases in empirical
validations, contexts of empirical validations, and open links to the proposed tools/frameworks.
The rest of this paper is organized as follows. Section 2 provides a summary of the related
studies. Section 3 overviews the study search and selection processes, and potential validity
threats. Section 4 reviews the primary studies in detail, and Section 5 evaluates them with
respect to basic characteristics of validation. Finally, Section 6 concludes the paper.
2. Related Work
A list of related secondary studies, which include primary studies on tools/frameworks for
AI-based software development, are provided in Table 1. As seen from the table, there is no
study that solely investigates, reviews in detail, and evaluates the tools/frameworks which
support the AI-based software development process. Therefore, ours is the first study that is
intended for this specific purpose.
3. Methodology
First, we have applied guidelines to conduct systematic literature reviews in software engineering
[17], and have searched and identified 85 primary studies that propose development models or
processes for AI-based software. In search and study selection, we have not focused on specific
phases of software development such as requirements or design; rather, we took a holistic view
for the software development process.
Table 1
Related secondary studies
Ref. Focus
[9] This survey study presents a comprehensive overview of recent and cutting-edge AI software,
including open-source frameworks and libraries, which support the implementation of ML
and DL algorithms. However, it does not specifically focus on the development process of
these algorithms.
[10] This study mines both academic and grey literature, identifies 29 engineering best practices
for ML applications, and conducts a survey among 300+ practitioners to determine the
degree of adoption of these practices. Although it refers to a number of tools in both white
and grey literature, its focus is not on tools/frameworks that support the development
process, and it does not include a detailed review.
[11] This survey describes major research efforts where ML systems have been deployed at the
edge of computer networks, focusing on the operational aspects including compression
techniques, tools, frameworks, and hardware used in successful applications of intelligent
edge systems. Yet, it does not focus on tools/frameworks to support the development or
deployment processes.
[12] This study summarizes secondary data from non-grey and grey academic literature. It
identifies, analyzes, and synthesizes the challenges of ML-enabled software development,
and re-frames the traditional SE development process to engineer the ML software devel-
opment process. However, it does not specifically review the primary studies that propose
tools/frameworks to support the development process.
[13] This study presents the results from a systematic mapping study on the life cycle of AI
models. It yields 405 primary studies, mapped in 5 different research topics. It reports that
only a minority of publications focus on data management and model production problems,
and that more studies should address the AI life cycle from a holistic perspective. This study
does not have a specific focus on tools/frameworks that support the life cycle of AI models.
[3] This study identifies and synthesizes the current state of software engineering (SE) research
for engineering ML systems by a systematic literature review of 141 primary studies. It
highlights that SE aspects do not have a mature set of tools and techniques. It reviews in
detail the primary studies under the knowledge areas of SWEBOK [14], including the ones
that propose tools/frameworks in each area. However, only few of these primary studies
support the development process of ML software.
[15] The study surveys existing efforts for DevOps, and categorizes them according to the life cycle
stages that they contribute. It outlines directions for future work in quality-aware DevOps,
in particular at AI for DevOps and DevOps for AI software. In terms of tools/frameworks, it
refers to only one primary study which proposes a software architecture solution that can
ensure the continuous development of computer vision applications.
[16] To synthesize knowledge on SE approaches for building, operating, and maintaining AI-based
systems, this study presents results from a systematic mapping of 248 primary studies. It
classifies their subjects to SWEBOK’s knowledge areas, including the "Software Engineering
Process" area and its supporting tools/frameworks. However, it only refers to these studies
in the classification scheme and does not include a detailed review.
3.1. Research Questions
Research questions (RQs) of this review study are described below:
RQ1. What is the type of the proposed tool/framework? (plugin, library, toolkit, etc.)
RQ2. For what purpose, theme (data, model, maturity, etc.) or domain (e.g., health) has the
tool/framework been proposed?
RQ3. Does the proposal include a tool? If yes, is the tool accessed publicly or is it open-source?
If yes, what is its url?
RQ4. Has the proposal been validated (with weak or strong empirical study)? If yes, what are
the research methods (case study, survey etc.) used in and the context (scope, etc.) of validation?
3.2. Search strategy and study selection
Search sentences that were run in digital libraries as of January 2022 are listed below:
1. ("artificial intelligence software" OR "AI software" OR "machine learning software" OR "ML
software") AND ("development approach" OR "development process" OR "development
model" OR "development life-cycle")
2. ("artificial intelligence applications" OR "AI applications" OR "machine learning applica-
tions" OR "ML applications") AND ("development approach" OR "development process"
OR "development model" OR "development life-cycle")
3. ("artificial intelligence software" OR "AI software" OR "machine learning software" OR "ML
software") AND ("software development" OR "application development") AND ("approach"
OR "process" OR "model" OR "life-cycle")
4. ("artificial intelligence applications" OR "AI applications" OR "machine learning applica-
tions" OR "ML applications") AND ("software development" OR "application development")
AND (approach OR process OR model OR life-cycle)
5. ("artificial intelligence software" OR "AI software" OR "machine learning software" OR
"ML software") AND (maturity OR capability) AND ("software development" OR "software
engineering") AND (approach OR process OR model OR life-cycle)
6. ("artificial intelligence applications" OR "AI applications" OR "machine learning applica-
tions" OR "ML applications") AND (maturity OR capability) AND ("software development"
OR "software engineering") AND (approach OR process OR model OR life-cycle)
7. ("artificial intelligence software" OR "AI software" OR "machine learning software" OR "ML
software") AND (challenges OR difficulties OR problems) AND ("software development"
OR "software engineering")
8. ("artificial intelligence applications" OR "AI applications" OR "machine learning appli-
cations" OR "ML applications") AND (challenges OR difficulties OR problems) AND
("software development" OR "software engineering")
The number of studies retrieved from the digital libraries by these search sentences are
given in Table 2. While identifying and selecting the primary studies, we have eliminated
studies applying inclusion and exclusion criteria in the order of the digital libraries shown in the
table. The inclusion criteria are: I1) papers focus on the holistic view of software development
process rather than specific phases of software development; I2) papers are in the area of
both software engineering and AI. The exclusion criteria are: E1) publication language being
other than English; E2) full-text not being available; E3) books and theses; E4) duplicate results
from different search methods, and E5) papers are explicitly short (e.g., less than six pages).
Until now, we have identified contributions made and research methods [18] employed in the
primary studies, which are listed in Table 3. Full details can be found in the study’s transparent
online spreadsheet (https://tinyurl.com/ytnxhhuy). Here, we should note that we are still in the
data extraction stage for the larger pool, and present the initial results from it on the basis of
supporting tools/frameworks in this paper.
Table 2
Number of primary studies retrieved and selected
Number of initially retrieved studies Number Number of
Sentence
of selected selected pri-
studies for mary stud-
AI-based ies to review
software de- in this paper
velopment
process
Web Scopus IEEE Springer ACM Google
of Sci- Xplore Link DL Scholar
ence
S1 5 116 706 196 128 3690 17 2
S2 26 52 302 141 259 7760 20 3
S3 22 33 237 345 215 6190 12 2
S4 33 85 27 915 408 10100 4 1
S5 5 8 44 395 232 3930 13 5
S6 4 9 8 1343 428 7540 4 0
S7 66 43 762 997 357 8370 10 1
S8 34 81 90 4065 609 14800 5 0
Total 15 8 15 4 38 5 85 14
Table 3
Types of contributions and research methods used in classifying primary studies
Types of contributions Types of research No.of studies per research
method in this paper
Tool/Framework (1) Solution proposal, (1) 5 (excluded in this paper),
Tasks/Phases (2) Validation research, (2) 6,
Workflow (3) Evaluation research, (3) 8,
Taxonomy (4) Experience paper. (4) 0.
Guideline
List of challenges
Solutions-to challenges
Language
Lessons learned
List of best practices
Empirical study only
In order to construct the study pool in line with the purpose of this paper, we have selected
14 primary studies that have a contribution type of tool or framework and a research method
type of validation or evaluation. In validation research, techniques investigated are novel and
have not yet been implemented in practice (used for example experiments, i.e. work done in
the lab) while in evaluation research, techniques are implemented in practice (i.e. solution
implementation) and an evaluation of the technique is conducted in terms of benefits and
drawbacks (i.e. implementation evaluation) [19]. The reason for selecting a subset of primary
studies with the stated characteristics is that, as we already mentioned in the introduction section,
we would like to identify, review and evaluate the empirically validated tools/frameworks that
support the development process of AI-based software.
3.3. Potential threats to validity
A number of validity threats are of concern for this study as adopted from [20, 21] in terms of
construct, internal, external and conclusion validity. The guideline for conducting SLRs has
been followed and a research protocol has been developed and reviewed to cope with construct
validity. One threat of this type regarding the applied protocol can be excluding the studies for
the specific phases of software development, such as requirements and design. However, we
have done this purposefully, since the subject area is quite broad and we have considered the
software development process as a whole. With this strategy applied, we have not only retrieved
the studies that propose development models or processes as a primary contribution but also
the studies that embed such contributions secondarily into their AI-based software development
efforts. Another threat of construct validity is related to the research method (i.e. SLR) that we
have employed in this study. Including the grey literature in addition to the white literature
could have elicited further proposals of tools/frameworks for the AI-based software development
process. We claim this threat so that it can be considered while reading our findings and also be
addressed in future studies. In terms of internal validity, we have applied independent voting
among authors for inclusion/exclusion of the studies and for quality assessment of initially
included studies. Additionally, we have carried out pilot data extraction and have conducted
regular meetings to ensure consistency and commonality during data extraction. Regarding
external validity, we admit that the results we obtained are valid only for the primary study
pool that we have created, and it cannot be generalized in other contexts. Finally, in terms of
conclusion validity, we have made several meetings to analyze and synthesize the results of
data extraction. Since we have used a standardized format (i.e. Google Sheet) in data analysis,
we believe other researchers with the same study pool would come up with similar results.
4. Review of Tools/Frameworks
In this section, we review the selected primary studies on tools/frameworks that support
AI-based software development process by grouping the studies under four headings. We
performed thematic synthesis [22] to group the studies as: software system development process,
development process of fair software, model development process, and model deployment and
operation processes. The studies falling under these headings are elaborated in the following
subsections.
Full details with respect to the research questions raised can be found in the study’s on-
line spreadsheet (https://tinyurl.com/ytnxhhuy) in the following columns, respectively: RQ1:
Type (AD); RQ2: Purpose (AO), theme (AR), domain (AV); RQ3: Link to download (AQ); RQ4:
Validation type (Z, AA), research methods (AB-AK), context (AV).
4.1. Tools/Frameworks That Support Software System Development Process
The primary studies grouped under this heading are summarized in Table 4, each with its
title, purpose and scope. As seen from the table, these three studies have different concerns,
which indicates that there is a diversity of the studies in this field. The first one is related to
health informatics, and we know there is a trend in applying AI techniques in this domain to
reveal important insights about healthcare data and healthcare processes. The second one is
about deep learning-based project development using the principles of MDE (Model Driven
Engineering) to solve the challenges to build the deep learning-based project development,
particularly for neural networks which have complicated architecture and dependencies. The
third one is seeking solutions for the scalability and performance problems for the development
of conversational AI software like mobile software, and proposes a framework inspiring Cell
theory and BDI (Belief-Desire-Intention) software model. These proposals can be considered as
example solutions to common problems in AI software development, and as the trending topics
for the researchers.
Table 4
Studies that support software system development process
Study, Purpose and Scope
Year
P1[23], - It investigates the interaction between software engineering and machine learning
2020 within the context of health systems.
- It proposes SEMLHI framework (the framework and methodology of software engi-
neering for ML in health informatics) that includes four modules: software, machine
learning, machine learning algorithms, and health informatics data.
- The SEMLHI methodology includes seven phases: designing, implementing, maintain-
ing and defining workflows; structuring information; ensuring security and privacy;
performance testing and evaluation; and releasing the software applications.
P4[24], - It supports deep learning based project development.
2021 - It proposes an ML-oriented artifact model inspiring Model Driven Development (MDE)
and the evaluation of the proposed concepts are combined into a Maven based build
infrastructure.
- A Maven plugin is developed to automate build which provides install and deploy goals
for all kinds of archives of the proposed artifact model.
P5[25], - It presents the CellS framework to improve smart software development on multicore
2021 mobile processor systems.
- CellS framework has five important components: anima, delibera, cell, plan and ligand.
- It is used in a resources-constrained mobile system for conversational AI software like a
software dialogue system.
4.2. Tools/Frameworks That Support Development Process of Fair Software
The primary studies grouped under this heading are summarized in Table 5. It is apparent from
the table that most of the studies focus on bias mitigation in ML software. Measurement of bias
is handled in a maturity framework from the organizational level by applying ethical guidelines
and standards, and fairness measurement is handled through a scalable measurement system
for computing fairness metrics. It is obvious that ethical AI activities should be addressed with
bias detection, bias/fair measurement, and bias mitigation methods starting from the initial
stages of the AI development life cycle.
Table 5
Studies that support development process of fair software
Study, Purpose and Scope
Year
P3[26], - It presents a maturity framework based on ethical principles and best practices to
2019 evaluate the organization capability in order to effectively govern AI bias.
- It provides a measurement of bias governance capability maturity of the organizations
by separating the scoring in two sections as "consideration" and "action".
P8[27], - It introduces Fairea, a model behaviour mutation approach for benchmarking and
2021 quantitatively evaluating bias mitigation methods for ML software.
- It includes three bias mitigation approaches: pre-processing, in-processing and post-
processing.
- There are three primary steps: baseline creation with model behaviour mutation,
bias mitigation effectiveness region division, and quantitative evaluation of trade-off
effectiveness.
P9[28], - It proposes a method named Fairway for bias detection and bias mitigation in binary
2020 classification models to remove ethical bias from training data and trained models.
- This handy tool combines two bias mitigation approaches: pre-processing (before model
training) and in-processing (while model training).
P11 [29], - It presents LinkedIn Fairness Toolkit (LiFT), a framework for scalable computation of
2020 fairness metrics as part of ML systems.
- It highlights the key requirements in deployed settings, and presents the design of a
fairness measurement system.
- LiFT comprises bias measurement and mitigation components that can be integrated
into different stages of an ML training and serving system.
4.3. Tools/Frameworks To Support Model Development Process
The primary studies grouped under this heading are summarized in Table 6. As seen from
the table, even though introduced models have different specific targets, they all have the
common focus on data contribution in model development. The first model focuses on the data
scientists’ cognitive workflows that include data collection. The second model introduces a
validation framework for data. The last model provides an open source library with different
data modalities.
Table 6
Studies that support model development process
Study Purpose and Scope
P6[30], - It focuses on observing and analyzing data scientists’ cognitive workflows as they
2021 develop predictive models.
- It proposes DSWorkFlow that covers data collection,workflow reconstruction, and
feature extraction stages.
P12[31], - It helps in systematizing the adoption of data validation processes and tools in industrial
2021 ML projects.
- It introduces a data validation framework (DVF) that includes validation process,
validation artifacts, data validation types, data validation tool setup, and feedback and
mitigation strategy.
P13[32], - It provides guidance to develop, and metrics to evaluate.
2020 - It comes up with Machine Learning (ML) Bazaar as open source libraries for components
to create general-purpose, multi-task, end-to-end AutoML systems that provide solutions
to different data modalities (image, text, graph, tabular, relational, etc.) and problem
types (classification, regression, anomaly detection, graph matching, etc.).
4.4. Tools/Frameworks That Support Model Deployment and Operation
Processes
The primary studies grouped under this heading are summarized Table 7. It is apparent from the
table that all studies for model deployment and operation take software, data and its machine
learning models into account together. That means pipeline and deployment of software are
not independent of data and machine learning models. In addition to that, the use cases are
limited and only selected models are used. They need to be applied in different domains with
real use cases to get wider insight for further usage areas.
5. Evaluation of Tools/Frameworks
After grouping and reviewing the primary studies in the previous section, we further evaluate
their proposals with respect to basic characteristics of empirical validations, which include
research method and type, domain and number of empirical cases, context of empirical cases,
and link to proposed tool/framework. Table 8 presents these characteristics for all the included
primary studies in columns.
As seen from the Table 8, the evaluation type of research (8) is slightly more than validation
type (6) in the primary studies. Case study research (in eight studies) and lab experiments
(in six studies) are the most frequently used research methods. While domains of empirical
validation are diverse, the number of empirical cases applied for validation is limited. Contexts
of empirical validation are various in alignment with the diversity of the domains. Finally, we
see that only half (7) of the primary studies provide links to their proposals as open source,
which is very important for the repeatability of the empirical validations as well as for further
validation studies by other researchers or practitioners.
Table 7
Studies that support model deployment-operation processes
Study, Purpose and Scope
Year
P2[33], - It proposes a framework for selecting a certain architecture.
2020 - It outlines the trade-off between device cost, model performance, and data privacy in
selection of architecture alternatives.
- Total of five different architectural alternatives exist from centralized cloud to fully
decentralized edge architectures. It explains seven case companies in the embedded
system domain.
P7[34], - It presents a framework for supporting collaborative data science development and
2021 open-source feature engineering.
- The framework’s name is Ballet that includes feature definition, feature engineering
pipeline, feature API validation, ML performance validation, and project management
capabilities.
- It presents two use cases, first one is in disease prediction and the other one is in income
prediction.
P10[35], - It introduces a development environment namely Gestalt that allows developers to
2020 implement a classification pipeline, analyzes data as it moves through that pipeline, and
supports easy transition between implementation and experiment.
- It explains 2 use cases: sentiment analysis, gesture recognition.
- It underlines the contributions of flexibility and visualization.
P14[36], - It introduces a cognitive hardware and software composed infrastructure.
2019 - The infrastructure is CHASE-CI for managing fast GPU appliances for machine learning
and storage managed through Kubernetes on the high-speed.
- It presents a software containerization approach and libraries for turning into a dis-
tributed computer for big data analysis.
- It explains a use case in earth science phenomena with object segmentation workflow.
In terms of the quantity of the cases included in empirical validation, the following studies
come to the fore, only the first one employing validation type research: P2 [33] (for model
deployment and operation processes) with seven cases from software intensive embedded
system domain, P9 [28] (for development process of fair software) with five cases regarding five
datasets used from UC Irvine ML Repository, and P13 [32] (for model development process)
with five cases from mixed domains including telemetry, healthcare, energy, water supply, and
data science.
6. Conclusion
In this study, we reviewed tools/frameworks within white literature in a systematic way for
tools/frameworks that support the AI-based software development process from a holistic
perspective. We selected and analyzed 14 primary studies and we grouped them under four
headings: software system development process, the development process of fair software,
model development process, and model deployment and operation processes.
Within these four groups, the scopes of the studies are seen as diversified, and data and model
dependency of the tools or frameworks is observed such as P1 proposing a framework for health
informatics whereas P5 is focusing on mobile systems. Additionally, data and machine learning
models have effects on the software development process, and only half of the studies include
the links to their proposals. On the other hand, more than half of the studies are presented with
evaluations while the rest of them use validation type research.
These findings underline the fact that the AI-based software development process is an
emerging field of research and needs further investigation. Furthermore, some of the proposed
tools or frameworks need to be sharpened and to be validated in different domains in various
industrial contexts. For researchers and practitioners, the next step can be the comparison of
new tools/frameworks with existing models in industry in terms of performance and quality
metrics. Additionally, a comparison of the tools/frameworks that support traditional software
development processes may provide valuable findings in the maturation of the overall software
development in the AI/ML area. As the last point, practitioners in industry can test the seven
of the tools/frameworks with their access links given in Table 8, evaluate their real-life per-
formances, and share their results publicly as the feedback to the community as well as the
researchers who introduced them.
References
[1] L. E. Lwakatare, A. Raj, J. Bosch, H. H. Olsson, I. Crnkovic, A taxonomy of software
engineering challenges for machine learning systems: An empirical investigation, in:
International Conference on Agile Software Development, Springer, Cham, 2019, pp.
227–243.
[2] F. Kumeno, Sofware engneering challenges for machine learning applications: A literature
review, Intelligent Decision Technologies 13 (2019) 463–476.
[3] G. Giray, A software engineering perspective on engineering machine learning systems:
State of the art and challenges, Journal of Systems and Software 180 (2021) 111031.
[4] A. Arpteg, B. Brinne, L. Crnkovic-Friis, J. Bosch, Software engineering challenges of deep
learning, in: 2018 44th Euromicro Conference on Software Engineering and Advanced
Applications (SEAA), IEEE, 2018, pp. 50–59.
[5] H. Belani, M. Vukovic, Ž. Car, Requirements engineering challenges in building ai-based
complex systems, in: 2019 IEEE 27th International Requirements Engineering Conference
Workshops (REW), IEEE, 2019, pp. 252–255.
[6] K. Sugali, Software testing: Issues and challenges of artificial intelligence & machine
learning, IJAIA (2021).
[7] D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young,
J.-F. Crespo, D. Dennison, Hidden technical debt in machine learning systems, Advances
in neural information processing systems 28 (2015).
[8] S. Amershi, A. Begel, C. Bird, R. DeLine, H. Gall, E. Kamar, N. Nagappan, B. Nushi,
T. Zimmermann, Software engineering for machine learning: A case study, in: 2019
IEEE/ACM 41st International Conference on Software Engineering: Software Engineering
in Practice (ICSE-SEIP), IEEE, 2019, pp. 291–300.
[9] G. Nguyen, S. Dlugolinsky, M. Bobák, V. Tran, Á. López García, I. Heredia, P. Malík,
L. Hluchỳ, Machine learning and deep learning frameworks and libraries for large-scale
data mining: a survey, Artificial Intelligence Review 52 (2019) 77–124.
[10] A. Serban, K. van der Blom, H. Hoos, J. Visser, Adoption and effects of software engineering
best practices in machine learning, in: Proceedings of the 14th ACM/IEEE International
Symposium on Empirical Software Engineering and Measurement (ESEM), 2020, pp. 1–12.
[11] M. S. Murshed, C. Murphy, D. Hou, N. Khan, G. Ananthanarayanan, F. Hussain, Machine
learning at the network edge: A survey, ACM Computing Surveys (CSUR) 54 (2021) 1–37.
[12] S. Saeed, M. M. Abubakar, M. Karabatak, Software engineering for data mining (ml-enabled)
software applications, in: 2021 9th International Symposium on Digital Forensics and
Security (ISDFS), IEEE, 2021, pp. 1–9.
[13] Y. Xie, L. Cruz, P. Heck, J. S. Rellermeyer, Systematic mapping study on the machine learn-
ing lifecycle, in: 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering
for AI (WAIN), IEEE, 2021, pp. 70–73.
[14] A. Abran, et al., Swebok, Guide to the Software Engineering Body of Knowledge (2004).
[15] A. Alnafessah, A. U. Gias, R. Wang, L. Zhu, G. Casale, A. Filieri, Quality-aware devops
research: Where do we stand?, IEEE Access 9 (2021) 44476–44489.
[16] S. Martínez-Fernández, J. Bogner, X. Franch, M. Oriol, J. Siebert, A. Trendowicz, A. M.
Vollmer, S. Wagner, Software engineering for ai-based systems: a survey, ACM Transac-
tions on Software Engineering and Methodology (TOSEM) 31 (2022) 1–59.
[17] B. Kitchenham, S. Charters, Guidelines for performing systematic literature reviews in
software engineering (2007).
[18] R. Wieringa, N. Maiden, N. Mead, C. Rolland, Requirements engineering paper classification
and evaluation criteria: a proposal and a discussion, Requirements engineering 11 (2006)
102–107.
[19] K. Petersen, R. Feldt, S. Mujtaba, M. Mattsson, Systematic mapping studies in software
engineering, in: 12th International Conference on Evaluation and Assessment in Software
Engineering (EASE) 12, 2008, pp. 1–10.
[20] C. Wohlin, P. Runeson, M. Höst, M. C. Ohlsson, B. Regnell, A. Wesslén, Experimentation
in software engineering, Springer Science & Business Media, 2012.
[21] X. Zhou, Y. Jin, H. Zhang, S. Li, X. Huang, A map of threats to validity of systematic
literature reviews in software engineering, in: 2016 23rd Asia-Pacific Software Engineering
Conference (APSEC), IEEE, 2016, pp. 153–160.
[22] D. S. Cruzes, T. Dyba, Recommended steps for thematic synthesis in software engineering,
in: 2011 international symposium on empirical software engineering and measurement,
IEEE, 2011, pp. 275–284.
[23] M. Moreb, T. A. Mohammed, O. Bayat, A novel software engineering approach toward
using machine learning for improving the efficiency of health systems, IEEE Access 8
(2020) 23169–23178.
[24] A. Atouani, J. C. Kirchhof, E. Kusmenko, B. Rumpe, Artifact and reference models for
generative machine learning frameworks and build systems, in: Proceedings of the 20th
ACM SIGPLAN International Conference on Generative Programming: Concepts and
Experiences, 2021, pp. 55–68.
[25] C.-H. Chen, M.-C. Wu, Cells: A cell-inspired efficient software framework for ai-enabled
application on resources-constrained mobile system, Electronics 10 (2021) 568.
[26] D. L. Coates, A. Martin, An instrument to evaluate the maturity of bias governance
capability in artificial intelligence projects, IBM Journal of Research and Development 63
(2019) 7–1.
[27] M. Hort, J. M. Zhang, F. Sarro, M. Harman, Fairea: A model behaviour mutation approach
to benchmarking bias mitigation methods, in: Proceedings of the 29th ACM Joint Meeting
on European Software Engineering Conference and Symposium on the Foundations of
Software Engineering, 2021, pp. 994–1006.
[28] J. Chakraborty, S. Majumder, Z. Yu, T. Menzies, Fairway: A way to build fair ml software, in:
Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference
and Symposium on the Foundations of Software Engineering, 2020, pp. 654–665.
[29] S. Vasudevan, K. Kenthapadi, Lift: A scalable framework for measuring fairness in ml
applications, in: Proceedings of the 29th ACM International Conference on Information &
Knowledge Management, 2020, pp. 2773–2780.
[30] M. Mash, S. Rosenthal, R. Simmons, Dsworkflow: A framework for capturing data scientists’
workflows, in: Extended Abstracts of the 2021 CHI Conference on Human Factors in
Computing Systems, 2021, pp. 1–7.
[31] L. E. Lwakatare, E. Rånge, I. Crnkovic, J. Bosch, On the experiences of adopting automated
data validation in an industrial machine learning project, in: 2021 IEEE/ACM 43rd Interna-
tional Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP),
IEEE, 2021, pp. 248–257.
[32] M. J. Smith, C. Sala, J. M. Kanter, K. Veeramachaneni, The machine learning bazaar:
Harnessing the ml ecosystem for effective system development, in: Proceedings of the
2020 ACM SIGMOD International Conference on Management of Data, 2020, pp. 785–800.
[33] M. M. John, H. H. Olsson, J. Bosch, Ai deployment architecture: Multi-case study for key
factor identification, in: 2020 27th Asia-Pacific Software Engineering Conference (APSEC),
IEEE, 2020, pp. 395–404.
[34] M. J. Smith, J. Cito, K. Lu, K. Veeramachaneni, Enabling collaborative data science de-
velopment with the ballet framework, Proceedings of the ACM on Human-Computer
Interaction 5 (2021) 1–39.
[35] K. Patel, N. Bancroft, S. M. Drucker, J. Fogarty, A. J. Ko, J. Landay, Gestalt: integrated
support for implementation and analysis in machine learning, in: Proceedings of the 23nd
annual ACM symposium on User interface software and technology, 2010, pp. 37–46.
[36] I. Altintas, K. Marcus, I. Nealey, S. L. Sellars, J. Graham, D. Mishin, J. Polizzi, D. Crawl,
T. DeFanti, L. Smarr, Workflow-driven distributed machine learning in chase-ci: A cognitive
hardware and software ecosystem community infrastructure, in: 2019 IEEE International
Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE, 2019, pp.
865–873.
Table 8
Evaluation of the primary studies with respect to basic characteristics of empirical validation
Study Related Related Research Domains of Context of empirical validation Link(s) to access
Section Re- Methods empirical val-
and search applied for idation (and
Theme Method empirical the number
Type validation of empirical
cases)
P1 [23] IV.A- Validation Case Study Healthcare (1) Three hospitals and nine medical centers Not available (N/A)
Sw.Sys. were used as the basis of the dataset.
Dev.
P2 [33] IV.D- Validation Case Study, Software Inten- 1) mobility solutions for vehicle devel- N/A
Model Interview, sive Embedded opment 2) services, software and infras-
Dep.Op. Workshop System (7) tructure in communication technology 3)
pumbs and electronics for pumb control
4) services in healthcare and energy 5)
packaging and processing solution for food
products 6) manufacturing and marketing
vehicles 7) systems for power generation,
transmission and medical diagnosis
P3 [26] IV.B- Evaluation Case Study, Data Science Three independent AI software projects N/A
Dev.of Interview, (3) were identified to demonstrate variances
Fair Sw. Ques.Survey between organizational size, industry,
project purpose and project stage.
P4 [24] IV.A- Validation Case Study Data Science 1) Evolution example (calculator that can N/A
Sw.Sys. (3) work with handwritten input) 2) Neural
Dev. network composition (sentiment of tex-
tual input) 3) Reference model-based train-
ing configuration (autonomous driving do-
main)
P5[25] IV.A- Evaluation Lab Experi- Software Sys- 1) hypothetical dialogue system 2) AI- N/A
Sw.Sys. ment tem (2) enabled dialogue application on resources-
Dev. constrained mobile system
P6 [30] IV.C- Validation Lab Experi- Data Science Seven data scientists, each created three https://github.com/
Model ment (7) machine learning models MoshikMash/DSWork-
Dev. Flow
P7 [34] IV.D- Validation Case Study, Healthcare (1), 1) lab exp. 1: disease incident prediction 2) https://github.com/ballet
Model Lab Experi- Real-estate lab exp. 2: house price prediction 3) case
Dep.Op. ment (1), Census- study: predict- census-income
income (1).
P8 [27] IV.B- Evaluation Lab Experi- Census- Three datasets in fairness literature: 1) https://github.com/ max-
Dev.of ment income (1), Adult census income 2) COMPAS (Correc- hort/Fairea/
Fair Sw. Criminal (1), tional Offender Management Profiling for
Finance (1). Alternative Sanctions) 3) German credit
data
P9 [28] IV.B- Evaluation Lab Experi- Census- Five datasets from UCI ML Repository: 1) https://github.com/ joy-
Dev.of ment income (1), Adult census income 2) COMPAS 3) Ger- mallyac/Fairway
Fair Sw. Criminal (1), man credit data 4) Default credit 5) Heart
Finance (2), health
Healthcare (1).
P10 IV.D- Validation Lab Experi- Data Science 1) sentiment analysis 2) gesture recogni- N/A
[35] Model ment (2) tion
Dep.Op.
P11 IV.B- Evaluation Real-World Data Science 1-3) Three web-scale ML pipelines at https://github.com/
[29] Dev.of Experiment (4) LinkedIn 4) Adult census income dataset Linkedin/LiFT
Fair Sw. from UC Irvine ML Repository
P12 IV.C- Evaluation Action Re- Tele- Classifying faults from returned hardware N/A
[31] Model search, Case communication telecommunication devices, in a large
Dev. Study (1) software-intensive organization.
P13 IV.C- Evaluation Case Study, Telemetry (1), 1) anomaly detection for satellite teleme- https://github.com/HDI-
[32] Model Real-World Healthcare (1), try 2) predicting clinical outcomes from Project/MLBlocks
Dev. Experiment Energy (1), Wa- electronic health records 3) failure predic- https://github.com/HDI-
ter Supply (1) tion in wind turbines 4) leaks and crack Project/BTB
Data Science detection in water distribution systems 5) https://github.com/HDI-
(1). DARPA D3M Program Project/AutoBazaar
P14 IV.D- Validation Case Study Atmospheric Object segmentation workflow (calculat- http://ucsd-
[36] Model Science (1) ing Integrated Water Vapor Transport from prp.gitlab.io/ nau-
Dep.Op. the assimilated meteorological field data tilus/namespaces/
archive)