Our research method for studying Ambiguity in Business Process Reengineering is thorough and organized. We start with clear goals and questions, shaping the focus of our study . To gather data, we carefully search academic
Databases using relevant keywords and Boolean operators . We set strict criteria to filter literature, concentrating on peer reviewed articles, conference papers, and major theses from the last decade. Screening occurs in stages, starting with title and abstract reviews and progressing to in depth examinations of full texts. We apply quality assessments to each chosen study to ensure our data is reliable. Data extraction is then carried out systematically, focusing on key methods, findings, and themes in each study. Our synthesis involves both qualitative and quantitative analysis, providing a comprehensive view of trends, challenges, and potential areas for future research in Ambiguity in Business Process Reengineering. This methodical approach strengthens the reliability and credibility of our research findings.
3.1 Research Questions
In the context of today's data driven landscape, the integrity and Ambiguity of software Business Process Reengineering have become crucial for the successful implementation of complex systems. This paper endeavors to dissect and integrate multifaceted research findings, with the aim of enhancing our understanding of software requirement specifications. We establish precise research questions that address the identification, utilization, and integration of tools and methodologies to improve the Ambiguity and interpretation of software Business Process Reengineering.
Table 1: Research Question
Code
|
Context
|
Question
|
RQ1
|
Agile User Stories and Ambiguity:
|
How tools that use human analysis can reduce Ambiguity in agile user stories for software Business Process Reengineering?
|
RQ2
|
Enhancing Ambiguity in Agile User Stories:
|
How Gamification and context-free grammar strategies improve Ambiguity in agile user stories for Business Process Reengineering?
|
RQ3
|
Crowdsourcing for Data Verification:
|
Can crowdsource help reduce Ambiguity and improve data quality in Business Process Reengineering?
|
RQ4
|
Visual Ambiguity in Software Business Process Reengineering:
|
What cutting-edge NLP and text mining techniques detect and resolve Ambiguity in Business Process Reengineering visuals?
|
RQ5
|
Feedback-Centric Tools in Continuous Software Engineering:
|
How do feedback-centric tools improve continuous software engineering to resolve the Complexity in BPR
|
3.2 Research Search Strategy
In our research journey to understand Ambiguity in Business process reengineering, we use a well organized and thorough method known as a Systematic Literature Review (SLR). To start, we set clear rules for what we include and exclude, focusing on peer reviewed articles, conference papers, and academic theses from the last ten years . Our search takes place in databases like IEEE Xplore, ACM Digital Library, Research Gate, Google Scholar and Springerlink.We use keywords like ' Ambiguity Identification ,' 'Feedback Driven,' 'User Stories,' 'Crowd Source,' 'Open Source,' 'Business Process Reengineering,' and 'natural language processing.' We document this search strategy carefully to make sure others can reproduce it. Once we gather the information, we go through a two step screening process. First, we quickly look at titles and abstracts, and then we do a more detailed review of the full texts for the studies that make the cut. We also use quality checks to make sure the studies we include are reliable and trustworthy. After that, we pull out the important information from each study—looking at how they did things, what they found, and their overall conclusions. We then analyze this data, looking for common themes, trends, and gaps in the research. This approach helps us review Ambiguity in Business Process Reengineering in a systematic, fair, and thorough way. It gives us a solid base for future research in this area .In our relentless pursuit of unraveling the intricate facets inherent in serverless architectures and Function as a Service (FaaS), a meticulously crafted and systematic research search strategy serves as the cornerstone of our scholarly exploration. This section provides a comprehensive insight into the methodological rigor employed to identify, select, and analyze relevant literature, ensuring a thorough understanding of this dynamically evolving field.The SLR protocol (Step 3, Fig:1) follows the PICOC criteria suggested by Kitchenham and Charter.
Table :2 Different Digital Source and Search Sting
Digital Libraries
|
String
|
IEEE Xplore
|
“Visual Ambiguity” AND “BPR” OR “Business Process Reengineering”
|
SpringerLink
|
“User stories” AND “ Ambiguity” AND “Software Engineering”
|
ResearchGate
|
“Crowdsourcing” AND “requirement Ambiguity”
|
ACM
|
“Business Process Reengineering” AND “Multilingual Ambiguity”
|
Google Scholar
|
“Software Requirement” AND “ Ambiguity in Voice”
|
Research across various digital libraries provides an in-depth examination of " Ambiguity" in software engineering and business processes (see Tables 2 and 3). IEEE Xplore addresses Visual Complexity in Business Process Reengineering (BPR), highlighting how visual tools can lead to misunderstandings. SpringerLink investigates ambiguity in user stories, emphasizing risks of unclear user stories in software engineering. ResearchGate explores how crowdsourcing affects requirement Ambiguity, examining variability in input from diverse contributors and its potential to introduce ambiguity. ACM investigates multilingual uncertainties within BPR, considering how language differences can challenge communication and documentation Ambiguity. Google Scholar focuses on the Ambiguity of software requirements in voice-based systems, addressing the unique challenges posed by ambiguity in spoken language.
Subtopics further detail Ambiguity issues: visual Complexity in BPR, ambiguity in user stories, crowdsourcing variability, multilingual communication challenges, and feedback-driven Complexity. These studies collectively emphasize the importance of addressing ambiguity to improve communication and outcomes, highlighting the need for clear, unambiguous requirements to ensure successful project execution and effective communication in software engineering and business process management.
Table :3 Different SubTopics and Search Sting
SubTopics
|
Search String
|
Visual Ambiguity
|
Visual Ambiguity AND BPR OR Business Process Reengineering
|
User stories
|
User stories AND Ambiguity AND Software Engineering
|
Crowdsourcing
|
Crowdsourcing AND requirement Ambiguity
|
Multilingual Ambiguity
|
Business Process Reengineering AND Multilingual Ambiguity
|
Ambiguity in Voice
|
Software Requirement AND Ambiguity in Voice
|
Feedback Driven Ambiguity
|
Feedback Driven OR Feedback Ambiguity AND requirement Ambiguity
|
Table 4:Primary Source and No of Papers by Year
Digital Source
|
No of Articles
|
Publishers
|
Year
|
IEEE Xplore
|
63
|
IEEE
|
2013-2023
|
SpringerLink
|
58
|
Springer
|
2010- 2023
|
ResearchGate
|
71
|
Research Gate
|
2008-2023
|
ACM
|
32
|
ACM Journal
|
2000-2023
|
Google Scholar
|
68
|
Elsevier, Wiley
|
2005-2003
|
In the fig:3 showed is a more visually appealing bar chart representing the number of publications in various digital libraries from 2000 to 2023,Each bar corresponds to a different digital library.The horizontal axis lists the libraries: IEEE Xplore, SpringerLink, ResearchGate, ACM, and Google Scholar.The vertical axis shows the number of publications for each library. The count of publications is displayed above each bar for easy reference.This enhanced graph provides a clear and colorful comparison of the publication output among these digital libraries over the specified time .
3.3 Inclusion and Exclusion Criteria
Before delineating the specific inclusion and exclusion criteria for our systematic literature review on Ambiguity in Business Process Reengineering, it is essential to establish a clear and objective framework. This framework is crucial to ensure that our review comprehensively encompasses relevant, highquality, and recent studies, thereby providing an accurate reflection of the current state of research in this field. The following criteria are meticulously designed to guide the selection process:
Inclusion Criteria:
1. Publication Date: Studies published within the last 10 years to ensure contemporary relevance and inclusion of the latest methodologies and technologies.
2. PeerReviewed Material: Only peer reviewed articles, conference papers, and academic theses to ensure the credibility and reliability of the information.
3. Focus on Ambiguity Identification : Studies specifically addressing Ambiguity in Business Process Reengineering, including theoretical frameworks, practical applications, and case studies.
4. Methodological Rigor: Research that demonstrates a clear and robust methodology, including empirical studies, systematic reviews, and qualitative analyses.
5. Language: Publications in English to ensure consistency in analysis and interpretation.
Exclusion Criteria:
1. NonPeerReviewed Material: Grey literature, including unpublished manuscripts, blog posts, and nonacademic articles, to maintain academic rigor.
2. Irrelevant Topics: Papers focusing on general software engineering or other aspects of Business Process Reengineering not directly related to Ambiguity Identification .
3. Outdated Research: Publications older than 10 years, except for seminal works that have laid the groundwork for subsequent research in this field.
4. Incomplete Studies: Research articles that are incomplete or have significant portions of their methodology or results undisclosed.
5. NonEnglish Publications: Studies published in languages other than English, due to potential challenges in accurate translation and interpretation.
These criteria are designed to ensure that the selected studies are relevant, credible, and contribute meaningfully to the understanding of Ambiguity in Business Process Reengineering. This approach facilitates a focused yet comprehensive review, allowing for an in depth analysis of the current state and advancements in the field.
Table 5: Inclusion and Exclusion criteria
Inclusion
|
Exclusion
|
Primary Studies like title, abstract, conclusion
|
Paper with less than 5 pages
|
Rappers from good journals and conference
|
Paper not in English language
|
Published date like Paper from 2000 and 2023
|
Duplicated studies
|
Paper must related with Business Process Reengineering
|
Redundant paper of the same author
|
Must have a result and proper solutions
|
Irrelevant research work and not match topics
|
3.4 Data Extraction and Synthesis
In the world of creating Enterprise software, getting the details right in Business Process Reengineering(BPR) is super important for building strong and dependable systems. As technology gets more complicated, there's a big need for better ways to find and deal with Complexity in software Business Process Reengineering. This paper looks at how human knowledge and fancy computer methods can work together to make software Business Process Reengineering clearer. Now, let's get into the important step of "Data Extraction and Synthesis." In this part, we're diving into lots of studies to pull out the key info and put it together to help us understand and improve how we find Ambiguity in Business Process Reengineering. We will explain exactly how we do this in the next section.
Table 6: Selection Phase and Selected article numbers
Source
|
Phase 1
|
Phase 2
|
Phase 3
|
Phase 4
|
IEEE Xplore
|
63
|
41
|
26
|
13
|
SpringerLink
|
58
|
35
|
18
|
9
|
ResearchGate
|
71
|
45
|
28
|
15
|
ACM
|
32
|
22
|
17
|
11
|
Google Scholar
|
68
|
39
|
27
|
12
|
Total
|
292
|
182
|
116
|
60
|
Fig:4 is the updated line chart with a more aesthetically pleasing color palette: Each line, representing a different digital library (IEEE Xplore, SpringerLink, ResearchGate, ACM, Google Scholar), is now colored distinctly: royal blue, sea green, dark orange, purple, and crimson respectively.The horizontal axis lists the phases: Phase 1, Phase 2, Phase 3, and Phase 4. The vertical axis shows the number of publications for each phase and library.This chart with its enhanced color scheme offers a clearer and more visually appealing comparison of publication trends across different phases for each digital library
Table 7:Approach to Ambiguity in Business Process Reengineering
Approach
|
No Of Article
|
Crowdsourcing Ambiguity in Business Process Reengineering
|
7
|
Ambiguity in Voice in Business Process Reengineering
|
5
|
Feedback driven Ambiguity In Business Process Reengineering
|
12
|
User Stories and Ambiguity In Business Process Reengineering
|
15
|
Ambiguity Severity Assessment in Business Process Reengineering
|
8
|
Visual Ambiguity Analysis in Business Process Reengineering
|
13
|
Total
|
60
|
The fig:5 presented here is a vibrant pie chart that visually breaks down the distribution of articles across different approaches in Business Process Reengineering engineering. Each segment of the pie chart corresponds to a distinct approach, employing the visually appealing viridis colormap for enhanced Ambiguity. The labels for each approach are thoughtfully provided, including:
- Crowdsourcing Ambiguity
- Ambiguity in Voice
- Feedback-Driven Ambiguity
- User Stories and Ambiguity
- Ambiguity Severity Assessment
- Visual Ambiguity Analysis
3.5 Study Quality Assessment
To create a Study Quality Assessment table for your research questions, we'll focus on key aspects such as the objectives, methodology, results, and relevance. This table will aid in evaluating the quality and applicability of studies related to your research questions. Below is an example format for the Table 6:
Table 8: Quality Assessment for the articles
ID
|
Yes
|
No
|
Partially
|
QA1
|
60 (100%)
|
0(0%)
|
0(%)
|
QA2
|
30 (50%)
|
20(33.33)
|
10(16.66%)
|
QA3
|
12(20%)
|
35(58.33)
|
13(21.66%)
|
QA4
|
21(35%)
|
42 (70%)
|
3(5%)
|
QA5
|
17(28.33%)
|
15(25%)
|
28(46.66)
|
In the "Results and Analysis" section, we delve into a comprehensive examination of the responses gathered from the research questions, QA1 through QA5. This segment of the study meticulously breaks down the distribution of answers—categorized as "Yes", "No", and "Partially"—providing a clear view of the participants' perspectives on each query. Through detailed analysis, we aim to uncover underlying patterns and insights, offering a deeper understanding of the complex dynamics at play in the response data. This analysis not only sheds light on the consensus areas but also highlights the aspects where opinions diverge, offering a nuanced understanding of the subject matter under investigation.
Fig: 6 is a stacked bar chart representing the responses to various research questions: Each bar corresponds to a different question (QA1 to QA5).The sections within each bar represent the responses: "Yes" (light green), "No" (salmon), and "Partially" (light blue).The height of each colored section indicates the number of responses for each category.This chart provides a clear visual representation of the distribution of responses across the different research questions, highlighting the proportions of "Yes", "No", and "Partially" answers for each question.
QA1: Unanimous Consensus
- Observation: QA1 stands out with a unanimous agreement, depicted by a solid light green bar. This indicates a clear consensus among respondents regarding the particular research question.
- Implication: The unanimity suggests that the topic addressed by QA1 is well-understood and widely accepted within the surveyed community.
QA2: Striking Polarization
- Observation: QA2 exhibits a noticeable polarization, with a significant divide between "Yes" and "No" responses, represented by distinct light green and salmon sections.
- Implication: The polarization in QA2 signals a need for further investigation or clarification. Understanding the reasons behind the divided opinions can uncover insights that may contribute to resolving discrepancies.
QA3 and QA4: Complexity and Nuance
- Observation: QA3 and QA4 show a higher proportion of partial agreements (light blue), indicating that respondents perceive these questions as more nuanced or complex.
- Implication: The prevalence of partial agreements suggests that QA3 and QA4 touch on issues with multiple facets, where respondents may acknowledge diverse perspectives or express Complexity.
QA5: Balanced Views
- Observation: QA5 displays a relatively even spread across all response options, indicating a balanced distribution of "Yes," "No," and "Partially" responses.
- Implication: This balance suggests that QA5 addresses a topic requiring consideration of multiple viewpoints or factors. The diversity in responses reflects a range of experiences or interpretations among survey participants.
Fig:7 are the pie charts for each of the research questions (QA1 to QA5), presented with a new, more visually appealing color palette: Each pie chart represents a different question, with the distribution of responses: "Yes", "No", and "Partially".The color scheme includes shades of pink , blue, and green, enhancing the visual appeal.The percentage in each segment indicates the proportion of each response type for that question.This layout with its attractive colors provides a visually engaging way to compare the response distributions across all the questions.
Fig:8 is the updated Pareto chart with a new color scheme:The bars now display in shades of pink, blue and green for the categories "Yes", "No", and "Partially", respectively.The cumulative percentage line is depicted in yellow. The left vertical axis measures the total number of responses in each category.The right vertical axis shows the cumulative percentage, indicating the proportion of responses accumulated up to each category.This chart with its enhanced colors provides a clear and visually engaging representation of the distribution and cumulative impact of the different response categories