# Exploring Deep Learning Methods for Real-Time Surgical Instrument
Segmentation in Laparoscopy

Debesh Jha12, Sharib Ali49, Nikhil Kumar Tomar1, Michael A. Riegler12,
Dag Johansen2, Håvard D. Johansen2, Pål Halvorsen14  1SimulaMet, Norway 2UiT
The Arctic University of Norway, Norway 3Oslo Metropolitan University, Norway
4Institute of Biomedical Engineering, University of Oxford, UK 9NIHR Oxford
Biomedical Research Centre, UK

###### Abstract

Minimally Invasive Surgery (MIS) is a surgical intervention used to examine
the organs inside the abdomen and has been widely used due to its
effectiveness over open surgery. Due to the hardware improvements such as high
definition cameras, this procedure has significantly improved and new software
methods have demonstrated potential for computer-assisted procedures. However,
there exists challenges and requirement to improve detection and tracking of
the position of the instruments during these surgical procedures. To this end,
we evaluate and compare some popular deep learning methods that can be
explored for the automated segmentation of surgical instruments in
laparoscopy, an important step towards tool tracking. Our experimental results
exhibit that the Dual decoder attention network (DDANet) produces a superior
result compared to other recent deep learning methods. DDANet yields a Dice
coefficient of 0.8739 and mean intersection-over-union of 0.8183 for the
Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at
a real-time speed of 101.36 frames-per-second that is critical for such
procedures.

###### Index Terms:

Deep learning, segmentation, minimally invasive surgery, surgical instruments,
laparoscopy, real-time

##  I Introduction

Minimally Invasive Surgery (MIS) is one of the most successful surgical
intervention methods performed by inserting the surgical instruments inside
the body of the patient through one or more small incisions [1]. Laparoscopy
is one of the types of MIS. It is a procedure with less pain, minor cuts,
reduced blood loss, shorter hospital stay, and fewer complications than
traditional open surgery, i.e., overcoming limitations of the traditional
laparoscopic surgery [2]. However, moving of the surgical instrument is
limited due to restricted operating space, which causes problems such as
limited field of view of the endoscope, reduction in surgeon’s dexterity, and
lacks perception of force feedback [3]. One of the solutions to overcome these
difficulties could be specialized surgical training of the surgeon for better
visualization [4]. Another solution could be the initiative to build computer
vision based applications to assist surgeons. A computer and robotic-assisted
surgical system can enhance the capability of the surgeons [5] and be helpful
for decision making during the surgery [6]. However, to make this possible,
the challenge of understanding the spatial relationships between surgical
instruments, cameras, and anatomy for the patient needs to be solved [7].

Segmentation of surgical instruments is a difficult task because it has
challenging conditions such as blood, smoke, reflection, and motion artifacts.
During surgery, a strong lightning condition is required for proper
visualization. The intense lights can lead to non-negligible specularity on
the tissue and the surgical instrument. Due to such specular reflections, the
surface color can be confused with other similar colored surfaces having the
same hue irrespective of having lower saturation [8]. In a surgical procedure,
specular reflections cause the surgical instrument to appear white.
Additionally, shadows can occur in the video frames due to the change in the
illumination angle and movement of the surgical tool during surgery [9].

Previous work has targeted instrument segmentation, detection, and tracking in
endoscopic video images. However, they fail on challenging images such as
images with the presence of specularity, blood, smoke, and motion artifacts
[10]. This study aims to find efficient computer vision methods for surgical
instrument segmentation from laparoscopic images. We evaluate several recent
state-of-the-art deep learning approaches with different backbones using the
ROBUST-MIS challenge dataset acquired from real world clinical surgical
procedures.

##  II Related Work

Surgical computer vision is evolving as promising techniques to segment and
track the instruments using endoscopic images are emerging [6, 10]. Bodenstedt
et al. [6] organized “EndoVis 2015 Instrument sub-
challenge111https://endovissub-instrument.grand-challenge.org/EndoVisSub-
Instrument/”, a part of Endoscopic vision (EndoVis)
challenge222https://endovis.grand-challenge.org/. In 2017, a similar
challenge, “Robotic Instrument Segmentation Sub-
Challenge333https://endovissub2017-roboticinstrumentsegmentation.grand-
challenge.org/” was organized at the same platform. At this time, the
organizers increased the number of tasks to three. The participants were asked
to participate in a binary segmentation task, a parts based segmentation task,
and an instrument type segmentation task. Both of these challenges provided
sufficient insights on the instrument segmentation. However, the insights
gained on the robustness and generalization capability of the deep learning
methods were limited [6, 10].

A similar Challenge “Robust Medical Instrument Segmentation Challenge
2019444https://robustmis2019.grand-challenge.org/” was organized by Roß et al.
[10]. The challenge aimed to develop a potential method for minimally invasive
surgery to track the surgical instruments in the abdomen. Most of these
methods included Mask-RCNN [11] based instance segmentation for multi-class
segmentation and for binary segmentation. Participants also explored methods
such as OR-UNet [12], DeepLabV3+[13], U-Net [14] and RASNet [15]. The best
performing methods for the binary instrument segmentation were OR-UNet and the
DeepLabv3+ with pre-trained ImageNet encoders. Ceron et al. [16] recently
proposed an attention-based segmentation for MIS instruments on the same
datasets that achieved state-of-the-art performance at nearly 45 frames-per-
second. In addition to these challenges, there are works on segmentation [17]
and identification of surgical instruments in laparoscopy [18]. We aim to
segment surgical instruments from laparoscopy in real-time with well-
established deep learning methods in this work.

##  III Material

###  III-A Dataset

ROBUST-MIS is part of the Heidelberg Colorectal (HeiCo) dataset [19, 10],
which includes a total of $5983$ images with their respective manually
annotated segmentation masks. These training images are obtained from $16$
different surgeries with two different types of procedures (prokto and rectum,
$8$ surgeries each). The prokto sub-folder consists of $2943$ images, and the
rectum sub-folder contains $3040$ images. Our experiments were performed on
the training dataset provided by the ROBUST-MIS Challenge organizers.

###  III-B Data Pre-processing

We have only considered the image dataset for training to reduce the
computational cost. The original size of the images is 960$\times$540\. We
split the dataset into (80-10-10) for training, validation, and testing,
respectively. The training dataset was augmented and resized. The total number
of images of the training dataset is 4787, and the validation and test dataset
contains 598 samples each.

###  III-C Experimental setup and configuration

All of the experiments were implemented using the PyTorch framework except
ResUNet [20] and ResUNet++ [21] that were implemented with TensorFlow [22].
The experiments such as ResUNet[20], ResUNet++ [21], ColonSegNet [23], DDANet
[24] were run on the Experimental Infrastructure for Exploration of Exascale
Computing (eX3), NVIDIA DGX-2 machine. These models were trained using offline
augmentation. The models such as UNet [14], FCN8 [25], PSPNet [26], DeepLabv3+
[13] with MobileNetv2 [27] and ResNet50 [28] backbones were trained on on
NVIDIA Quadro RTX 6000 and an online augmentation strategy was adopted. This
is because the experiments were run on two different GPUs. Adam optimizer was
adopted for all experiments. The other hyperparameters were tuned based on the
empirical evaluation.

TABLE I: Evaluation of surgical instruments segmentation task on the ROBUST-MIS Challenge 2019 dataset Method | Backbone | DSC | mIoU | Recall | Precision | F2 | Accuracy | FPS
---|---|---|---|---|---|---|---|---
UNet (MICCAI 2015) [14] | ResNet34 [28] | 0.4214 | 0.3318 | 0.5071 | 0.4224 | 0.4502 | 0.9294 | 35.00
FCN8 (CVPR 2015) [25] | - | 0.7774 | 0.6825 | 0.7737 | 0.8481 | 0.7600 | 0.9817 | 24.91
ResUNet (GRSL 2017) [20] | - | 0.7097 | 0.6193 | 0.8056 | 0.7627 | 0.7111 | 0.9755 | 10.40
PSPNet (CVPR 2017) [26] | - | 0.8110 | 0.7235 | 0.7677 | 0.9278 | 0.7715 | 0.9855 | 16.80
Deeplabv3+ (ECCV 2018) [13] | MobileNetv2 [27] | 0.8497 | 0.7780 | 0.8338 | 0.9246 | 0.8281 | 0.9882 | 32.50
Deeplabv3+ (ECCV 2018) [13] | ResNet50 [28] | 0.8582 | 0.7895 | 0.8397 | 0.9301 | 0.8362 | 0.9889 | 27.90
ResUNet++ (ISM 2019) [21] | - | 0.8252 | 0.7576 | 0.8460 | 0.8903 | 0.8319 | 0.9811 | 8.13
ColonSegNet (IEEE Access 2021) [23] | - | 0.8495 | 0.7943 | 0.8899 | 0.8871 | 0.8443 | 0.9898 | 185.54
DDANet (ICPRW 2020) [24] | - | 0.8739 | 0.8183 | 0.8703 | 0.9348 | 0.8613 | 0.9897 | 101.36
Figure 1: Qualitative results comparison of nine deep learning methods on easy
cases, medium cases, and hard cases

##  IV Methodology

The goal of a segmentation method is to label each pixel of the image into
either a surgical instrument or background, i.e., the output of the model is a
pixel-by-pixel array that determines the class of each pixel. We have compared
nine different state-of-the-art recent deep learning methods such as UNet
[14], FCN8 [25], ResUNet [20], ResUNet++ [21], DDANet [24], ColonSegNet [23],
PSPNet [26], and DeepLabv3+ [13] with ResNet50 [28] and MobileNetv2 [27]
backbones. The main motivation behind choosing these networks is that they
have shown state-of-the-art results for various medical image segmentation
tasks. A detailed explanation about the overall architectures, idea behind
using each component in the architecture, advantage of the blocks used, and
performance on different imaging modalities can be found in their respective
papers.

##  V Results

In this section, we present the experimental results. We report the result in
Table I using standard metrics used for computer vision tasks. From Table I,
we observe that DDANet produces the highest dice coefficient (DSC) of 0.8739,
mean Intersection over Union (mIoU) of 0.8183, recall of 0.8703, precision of
0.9348, F2 of 0.8613, accuracy of 0.9897 and real-time speed of 101.36. The
other most competitive method was ColonsegNet, as it has the competitive DSC,
mIoU, precision, and F2. ColonsegNet also has the highest recall of 0.8899 and
highest FPS of 185.54. The precision for DeepLabv3+ with ResNet50 backbone is
the best. From the quantitative result analysis, we can see that there is a
significant improvement in deep learning methods as we compare methods from
2015 to 2021. The current methods are efficient in terms of both accuracy and
speed.

To show the effectiveness of the different models, we have shown the
qualitative analysis results of nine different deep learning methods on easy,
medium, and hard images as shown in Figure 1. From the qualitative analysis,
we can see that all the models perform satisfactorily on the easy cases,
although it has one instrument or multiple instruments (Figure 1). For the
medium cases, the deep learning methods such as Deeplabv3, ColonsegNet, and
DDANet are performing satisfactorily. However, for ColonsegNet and DDANet, we
can also observe over-segmentation. For the hard cases, Deeplabv3 with both
the backbones shows better segmentation results for some scenarios, whereas
ColonSegNet and DDANet show over-segmentation. For some challenging scenarios,
DDANet shows promising results (please refer to the last image of Figure 1).
Overall, the preferred choice is DDANet, although it shows over-segmentation
for some scenarios. The strength is also because of its capability to segment
surgical instruments in real-time with a high FPS that was not achieved by
other methods such as DeepLabv3+ with different backbones.

##  VI Discussion

MIS is a progressive field where computer-assisted techniques are being
iterated and developed for clinical use. While, segmentation of surgical
instruments can play a vital role in tracking and a step forward towards
automated systems, the real-time requirement hinders the clinical
applicability of many recent deep learning methods. We have presented
different recent segmentation methods and identified efficient architectures
for the pixel-wise segmentation of surgical instruments on the laparoscopy
dataset. As observed from Table I, DDANet architecture provides the highest
metric performances over other methods and has real-time capability (101.36
FPS). As evident from Figure 1, DDANet is a robust encoder-decoder network
that can capture sharper surgical instrument boundaries by continuously
retrieving the spatial information. The encoder branch in the DDANet helps
boost performance by generating the attention map (spatial attention), which
is used in the segmentation branch to improve the feature representation.
Since the ground truth is not available for the test set, we can not directly
compare the results with the challenge participants. However, our results on
the validation set show promise and reliance of the DDANet. Furthermore, we
also explored the ability of models on a low-end NVIDIA 1060 Ti GPU (6 GB).
For this, we achieved an FPS of 120.05 on ColonSegNet and 95.05 on DDANet
which suggests the effectiveness of these models.

We explicitly choose to use a pre-trained encoder with the DeepLabv3+ because
we hypothesize that the ImageNet pre-trained network will improve performance.
Although it provided a promising DSC of 0.8582, the methods such as
ColonSegNet and DDANet showed competitive or better results without learning
from any pre-trained encoders. The main reason for higher FPS for these two
models is the lightweight network architecture that making the model efficient
for real-time segmentation facilitating clinical deployment. The detailed
analysis of the qualitative results (Figure 1) show that the best methods are
robust to samples with specularity, blood, and smoke, although it shows some
areas with over segmentation. This is also evident from the dice score of the
best performing method. However, the challenge still exists with the images
having moving instruments and the over/under segmentation problem with the
hard cases (see bottom rows, Figure 1) that need to be addressed in further
study.

##  VII Conclusion

This paper explores several deep learning methods with different backbones for
the segmentation of surgical instruments in laparoscopy. A comparison of
different state-of-the-art methods shows that DDANet is the most efficient
method providing a strong baseline on the ROBUST-MIS challenge 2019 dataset.
DDANet provides the most accurate segmentation of the surgical instruments
with a real-time speed of 101.36 FPS. The provided comparison shows the
strength of simple and small networks such as ColonSegNet and DDANet to
achieve real-time performance with competitive scores compared to that of
complex and hybrid networks. In the domains where real-time inference is
vital, exploring such networks are important to accelerate both research and
clinical applicability of models. The achieved results also highlight the
potential for the extension of the work to achieve higher accuracy by
incorporating attention mechanisms or use of skip-connections during the
feature flow but maintaining the speed at the required real-time performances,
e.g., 60 FPS for most devices. We plan to investigate multi-instance
segmentation and a composite instrument tracking technique for future work.

## Acknowledgement

D. Jha is funded by Research Council of Norway project number 263248
(Privaton). The computations in this paper were performed on equipment
provided by the Experimental Infrastructure for Exploration of Exascale
Computing (eX3), which is financially supported by the Research Council of
Norway under contract 270053.

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  *[MIS]: Minimally Invasive Surgery
  *[DSC]: dice coefficient
  *[mIoU]: mean Intersection over Union


# Can Artificial Intelligence Transform DevOps?

Mamdouh Alenezi, Mohammad Zarour, and Mohammad Akour
Software Engineering and Disruptive Innovation (SEDI)
College of Computer and Information Sciences
Prince Sultan University
Riyadh, Saudi Arabia
<EMAIL_ADDRESS>
###### Abstract

DevOps and Artificial Intelligence (AI) are interconnected with each other.
DevOps is a business-driven approach to providing quickly delivered quality
software, and AI is the technology that can be used in the system to enhance
its functionality. So, DevOps teams can use AI to test, code, release,
monitor, and improve the system. Through AI, the automation process delivered
by DevOps could be improved efficiently. This study aims to explore how AI can
transform DevOps. The research is useful in terms of facilitating software
developers and businesses to assess the importance of AI in DevOps. The study
has practical implications as it elaborates on how AI transforms DevOps and in
what way it can support businesses in their business.

_K_ eywords Software Engineering $\cdot$ DevOps $\cdot$ Artifical Intelligence

##  1 Introduction

DevOps and Artificial Intelligence (AI) are interconnected with each other.
Trivedi [1] proclaimed that DevOps is a business-driven approach to providing
software, and AI is the technology that can be used in the system to enhance
its functionality. So, DevOps teams can use AI to test, code, release,
monitor, and improve the system. Through AI, the automation process delivered
by DevOps could be improved efficiently. Besides, Battina [2] articulated that
AI and DevOps are interdependent for the better performance of information
systems. The author propounded that AI is significant for DevOps to perform
better organizational functions. However, businesses lose focus on harnessing
AI and DevOps to develop organizational systems better. In addition, Columbus
[3] discussed the ten ways AI can accelerate DevOps. The ten ways include
enhancing the productivity of DevOps in real-time, streamlining the
requirement management system, providing an effective bug detection system,
prioritizing the security testing and results, improving software quality,
providing adept troubleshooting, removing anomalies that can alter DevOps
teams, enhancing supply chain management, improving traceability, and creating
integrated DevOps strategy [4, 5]. Moreover, [3] articulated that it is
paramount to discuss the transformation of DevOps through AI because it can
help businesses realize its importance before it becomes late for them. So,
the research motivation is to achieve the aim of the study. The study aims to
assess whether AI can transform DevOps. The research can facilitate businesses
and software developers to know the significance of AI in DevOps. They can use
the study findings to elaborate on how AI transforms DevOps and how businesses
can adapt it for their success. Hence, AI and DevOps are interconnected and
can provide comprehensive benefits to businesses. The research shed light on
discussing how AI can transform DevOps as a primary motivation.

###  1.1 DevOps: A Brief Overview

DevOps is the ability of an organization to deliver applications and services
at high velocity. Chapman [6] articulated that DevOps combines engineering
patterns and practices, culture, and tools that can enhance the organizational
capability to deliver services with increased quality. Different practices
such as continuous integration, continuous delivery, monitoring and logging
and infrastructure as code have evolved during DevOps adaption. Jha and Khan
[7] propounded that the objectives of DevOps include building up a culture of
coordinated efforts, quickening time to advertise client input, keeping up
with quality standards, considering experimentation, mechanizing conventional
operational improvement, and using stages to give turnkey scenarios.
Initially, DevOps was considered helpful for big businesses, but now every
business is using DevOps to benefit from automation. In addition, Akshaya et
al. [8] posited that ’Dev’ means all developers involved in the production
stage and ’Ops’ means operation team, including security professionals,
administration, system engineers and others. So, DevOps is an activity where
developers and operational bodies collaborate in each DevOps cycle, starting
from the development to the production stage. Hence, DevOps combines
engineering, cultural, and IT activities to automate business operations or
product delivery. It involves developers and operations together to enhance
the quality of output [9, 10].

###  1.2 Artificial Intelligence (AI): A Brief Overview

Artificial intelligence (AI) is the intelligence demonstrated by machines
rather than humans. Ertel [11] proclaimed that AI, also known as machine
intelligence, demonstrates learning and intelligence in solving problems. In
the information technology field, AI can be considered an intelligent agent
who perceives the environment intelligently and takes actions to maximize the
chances of achieving goals. As per the author, AI has entered our daily lives
and is used by big organizations, for example, IBM, Uber, Google, Facebook,
and others, to enhance the automated systems, i.e., DevOps, through innovation
and continuous improvement promises. In a similar context, Flasiński [12]
elucidated that artificial intelligence is a logic-based approach. The AI
logic represents the knowledge of the world’s agent, its goals, and
situational analysis through logic. It informs the agent about the current
scenario that help in effective decision making. It reduces the human effort
in making the decision. Hence, artificial intelligence is a logic-based
natural computational language that perceives the environment and solves
problems with intelligence and learning. It facilitates monitoring, assessing,
and evaluation of systems and provide solution options to agents, which
reduces decision-making time drastically.

##  2 Literature Review

###  2.1 How do AI and DevOps work together?

Artificial intelligence and DevOps are interconnected with each other.
Kinsbruner [13] articulated that DevOps is about accelerating the software
development process to deliver value. It enhances the collaboration between
operation and developers to deliver the enhanced quality service to the
client. Traditional DevOps only allowed organizations to implement CD/CI
pipelines, but contemporary DevOps, in collaboration with AI, improved the
operations and automated the code reviews. AI/ML automated DevOps’ code
reviews and analysis process and lessened the burden on teams. It facilitates
to get earlier detection results of code flaws, security issues, and code-
related defects that can solve in time and enhances the end-user performance
results. In a similar context, Matsui et al. [14] and Surya [15] articulated
that AI transforms DevOps by enhancing the application’s quality of
development, deployment, and monitoring. It adds value to the DevOps cycle
through automated feedback and reviews. The tasks are completed in less time,
which lessens the delivery time of the application and increases the end-user
satisfaction. Additionally, Ciucu et al. [16] propounded that AI integrated
with TensorFlow (TF), Caffe, Apache, and Microsoft Cognitive Toolkit in the
past few years. Such DevOps deals with environment setup and database
management. The integration of AI enhanced the quality of DevOps processes
quality. For example, in a container pipeline, the implication of AI automates
the monitoring of API-Calls, debugging information, and process states. Hence,
AI and DevOps can work together by transforming DevOps processing time and
enhancing the quality.

###  2.2 DevOps and AI as new Standard of Businesses

It has been demonstrated that DevOps and AI can work together to enhance the
efficiency of businesses. Battina [2] articulated that AI and DevOps are
changing every industry by bringing automation and enhanced problem-solving
solutions. The use of AI in mobile robots increases DevOps and AI to convert
manual jobs to automated ones. They become the new standard of businesses
without which it is impossible to succeed in the market [17]. Moreover,
Columbus [3] discussed the ten ways AI can accelerate DevOps to enhance
business efficiency. The ten ways include enhancing the productivity of DevOps
in real-time, streamlining the requirement management system, providing an
effective bug detection system, prioritizing the security testing and results,
improving software quality, providing adept troubleshooting, can remove
anomalies that can alter DevOps teams, enhancing supply chain management,
improve traceability, and create integrated DevOps strategy. Hence, DevOps and
AI are the future of businesses. AI is bringing significant changes in DevOps
that can help businesses to achieve a competitive advantage in the market.

###  2.3 Benefits and Challenges

Integrating AI and DevOps can create overwhelming benefits and brings some
challenges. The benefits of AI include facilitating the DevOps team in
testing, coding, releasing, and enhancing the quality of programs. It converts
the manual coding and reviewing to an automated system that decreases
application delivery time and enhances problem-solving skills [2].
Additionally, 40% of businesses will use AI and DevOps by 2024 because AI
benefits businesses through transforming DevOps (Gartner). The transformation
includes alleviating operational complexities, streamlining communication,
simplifying application monitoring, guiding the approach for effective
application development, fostering resolutions, and improving software
testing. However, Hurst [18] articulated that AI can distract the engineering
team from end goals, bringing challenges in achieving the results. The narrow
task intelligence or weak AI can provide risks by limiting the decision-making
to a narrow set of statistics. So, developers and operational teams also need
to use their understanding in decision-making. Hence, AI can transform DevOps
to create overwhelming benefits for organizations. However, dependency on AI-
driven statistics can bring challenges for organizations that need human-
driven intelligence to control during the DevOps cycle of application
delivery.

##  3 Research Methodology

In consequence of the stated objective of the study, the following are the
preferred research methods. A qualitative method is applied for an in-depth
and broader analysis of AI transforming DevOps. This method is useful in
studies that demand non-statistical analysis. However, the quantitative
method, on the other hand, is not preferred as it largely relies on using
numerical, statistical and computational techniques [19], which do not
correlate or are required for the present study. In relation to the data
sources and data collection process, secondary data sources are preferred,
which are easily available and accessible online, including books, articles,
journals, websites, etc. However, the use of primary data sources is not
needed in this study as it focuses on retrieving information and data from a
group of people or first-hand sources. Moreover, primary sources are lengthy,
time-consuming and expensive, and it fits well usually with the quantitative
method in which surveys as a design are used [19]. Therefore, relating to the
present study, secondary sources are significant as they are not expensive,
neither time-consuming nor difficult to interpret. Moreover, the information
collected through secondary sources can be easily molded to suit the
objectives of the study, while in primary sources, this cannot happen as the
researcher have less control over the data/information. Hence, this secondary
source for data collection is suitable to rely on online websites. In order to
analyze the collected data, review-based analysis is considered, which is
appropriate for comprehensively reviewing literature related to the topic and
using it to support the key findings of the study [20]. In relevance to the
qualitative method and secondary sources, review-based analysis is well-match
and therefore is significant to accomplishing the key research objectives.

##  4 Results and Discussion

The findings of this study have focused on AI and DevOps integration, its link
with business standards, and benefits and challenges. It is noted that both AI
and DevOps are reliant as DevOps is a business-centric approach for delivering
software, and the technology of AI can be connected to the system to increase
functionality. Using AI, it is noted that DevOps can be tested, coded,
released and can efficiently monitor software. AI is found useful in improving
automation, addressing issues by identifying them quickly, and improving
collaboration [1]. The findings of Ertel [11] correlate with the results
arguing that AI is capable of solving problems and can be considered as an
able instrument through which timely actions can be taken to increase the
prospects of accomplishing key objectives.

Moreover, the results also highlight that AI can have an important role in
increasing the efficiency of DevOps, as it can increase performance by
allowing quick operation and development of cycles and giving a compelling
experience to customers. There are different ways through which AI can
transform and work together with DevOps. Limited unregulated access to data is
identified to be one of the key challenges encountered by DevOps teams, and
therefore in this reference, integrating with AI can aid in free data from its
company silos for data aggregation. In addition, AI is found to collect data
from different sources and categorize it for repeatable and consistent
analysis [1]. Likewise, the study of Kinsbruner [13] somewhat discussed
similar that AI automated DevOps’ analysis process facilitates teams and helps
address issues and increase performance.

Similarly, the results showed that Machine learning (ML) models could be
helpful in case of failure in areas/tools in DevOps, which can slow down and
weaken the process. ML can support predicting error using data, and AI can
predict signs and read patterns of failure, particularly when a fault is
identified. In addition, AI is useful in seeing indicators that cannot be
perceived by humans, and early notification and prediction can help teams to
acknowledge and fix the problem prior to it can affect SDLC (software
development life cycle) [1]. Ciucu et al. [16] support that the integration of
AI increases the quality of DevOps processes, and AI and DevOps can be
integrated by transforming the processing time of DevOps and increasing the
quality. ML can enhance the performance of DevOps by reducing inefficiencies
in SDLC and automating recurrent tasks. In addition, by using AI, the teams of
DevOps can check and test software more efficiently and quickly.

With respect to the standard of business, the results showed that with the
inclusion of ML and AI, the organization/business could be transformed
digitally. The integration of AI and ML with DevOps results in significant
shifts in its development, as initially, its sets DevOps as key support for
the digital transformation of the business. For the organizations relying on
living data, using ML and AI with DevOps is proven to increase their value
greatly in different aspects, such as from efficient workflow to the
strengthening of security for the development of applications. Considering the
involvement of ML and AI, the automation and manual configuration of security
aspects is fixated upon reducing the administration misconfiguration and
changes of faults. Improvisation can be made by reducing downtime, and gaps
can be done by assessing risks. Using ML and AI, data can be made efficient,
as well as decision-making and analysis.

A previous study by Battina [2] also highlighted that AI, and DevOps’
collaboration and integration can increase business efficiency, and these can
significantly affect the industry through automation and increase problem-
solving solutions. It has been found that AI can increase DevOps for enhancing
the efficiency of the business as well as its productivity by focusing on
streamlining the essentialities of the management system, providing a bug
detection system, and prioritizing the result and security test. Moreover, AI
is observed to bring considerable transformation in DevOps that can support
businesses in terms of their efficiency and competitiveness.

Regarding the benefits and challenges, the results show that AI is beneficial
in DevOps in terms of increasing the security of software. It does by raising
the speed of security tests. Besides, it is observed that software developers
are confused and in a position of indecisiveness when choosing to work on
time-consuming and necessary security tests. However, by using AI, the
software developers can easily run security tests and, at the same time,
complete their work on deadlines. This is possible because AI can
significantly lower the risk vector ID times, raising the efficiency of false-
positive identities. Moreover, it has an important role in terms of the
management of ill-trained security professionals, which is now a major problem
for organizations.

Moreover, AI can make it done by assisting cybersecurity professionals and
creating the demand for different types of security data professionals that
are able to work using AI technologies. The findings of Battina [2] also agree
that integrating AI can help to transform DevOps by reducing operational
complexities, streamlining communication, improving software testing,
simplifying monitoring of applications, fostering resolutions, and alleviating
operational issues. On the contrary, there exist challenges as well, and it is
found that a high level of complexity is linked to monitoring and handling the
DevOps environment. Besides, there is a challenge for DevOps teams to manage
the level in the contemporary distributed and dynamic application environment.
The DevOps team has to manage data in Exabyte, and hence it turns out to be
complex for people to manage such large data and address customer concerns, as
it is a time-consuming and challenging task. Recent literature by Hurst [18]
highlights the same and argues that it can be challenging for humans to manage
data. Moreover, it is noted that relying on AI-driven statistics can be
complex for companies that require human-driven intelligence to control the
DevOps cycle of application delivery.

##  5 Recommendations

The following are the key recommendation in terms of AI transforming DevOps:

It is recommended to rely on AI for efficient application progress as the use
of AI with instruments such as Git can give visibility to deal with
irregularities in even resource handling, long build time, and code volume.
Moreover, Ops teams for early detection can take the help of ML and AI as it
allows detection of issues through quick mitigation response enabling business
continuity. Moreover, AI can be significant in business assessment as well and
helpful for ensuring business continuity for companies and facilitating
business development. DevOps teams should use ML for emergency addressing as
it can help assess machine intelligence and has a significant role in managing
quick alerts by facilitating the system to point out the anomaly, therefore,
aiding in the filtering process of unexpected alerts making it highly
effective. Moreover, ML can be used in the production cycle and is found
useful in assessing resource utilization.

Likewise, AI can help transform DevOps through improved data access;
therefore, it is recommended that it integrate AI as it facilitates compiling
data from different sources and organizing it simultaneously. Moreover, it can
be helpful in analyzing and providing an overview of trends. Moreover, AI can
help in security as well; by using ML and AI, security threats can not only be
identified but also managed successfully. Besides, AI can be proven effective
in terms of bug detection; therefore, its application is recommended in this
reference.

Columbus [3] discussed that bug detection instruments at Facebook predicted
faults and recommended solutions that proved to be 80% correct, with AI
instruments adapting to automatically fix the bugs. It is noted that Semmle
Code QL is found to be a prime AI-centric DevOps instrument, and the Ops team
can rely on this tool for tracking risk in code and identifying logical
variants in the codebase. To add further, Microsoft has already used Code QL
for identifying risks and vulnerabilities. Experts of security in the
Microsoft team used this instrument to search for variants of major issues,
enabling them to locate and respond to major code issues and avert further
occurrences. Moreover, AI needs to be used by DevOps teams to analyze the
success of projects in terms of supply chain management. AI with ML algorithms
can find the pattern and acquire insight about the data and, therefore, can
significantly aid DevOps teams.

##  6 Conclusion

The study aimed to explore how AI can transform DevOps. The research is useful
in terms of facilitating software developers and businesses to assess the
importance of AI in DevOps. The study has practical implications as it
elaborates on how AI transforms DevOps and in what way it can support
businesses in their business. A plethora of literature has been reviewed
explaining AI and DevOps, its integration and its benefits and challenges. The
research adopted a qualitative method, secondary data source and review-based
analysis. It is noted that DevOps, in collaboration with AI, is useful in
improving operations and automating code reviews. Moreover, the automated code
review and analysis process are likely to reduce the burden on DevOps teams
and support them in getting early detection outcomes of code errors, security
problems and code-associated defects.

It is found that AI is important in increasing DevOps efficiency through quick
operation and development of cycle. Incorporating both ML and AI with DevOps
leads to considerable transformation in its development, and is increases
value and ensures an efficient flow of work. Moreover, AI can help software
developers to run security tests and timely complete work before deadlines.
Nevertheless, the challenge related to AI is about managing data in Exabyte,
which is complex for software developers and DevOps to manage, as it is quite
a challenging task. Besides, key recommendations are also offered to
businesses and software developers that can help them transform DevOps,
including efficient application, ensuring business continuity, managing quick
alerts, improved data access, aid in security, effective in bug detection, and
Code QL.

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