Machine learning literature

Machine learning literature

6/15/ · It should be noted, however, that much of the machine learning literature consists of customization and tuning of base models. Therefore, models are listed as their basic form in Table 18 and Table 19, even though some authors call them by different names once they alter or hybridize them according to their blogger.com by: 75 This SLR revealed that machine learning algorithms, specifically SVM (SL algorithm), possess great potential in the RE domain, as they proved to have considerably better performance, LDA (USL algorithm) is the most widely used machine learning algorithm, and the precision and recall are amongst the commonly utilized evaluation methods to measure the performance of these machine learning Cited by: 2 literature review of machine le arning techniques to analyse FLIGHT DATA S Kumar Jasra 1, J Gauci 1, A Muscat 2, G Valentino 3, D Zammit-Mangion 1 and R Camill eri 1

Machine Learning in Orthopedics: A Literature Review

Arshad Ahmad, Chong Feng, Muzammil Khan, Asif Khan, Ayaz Ullah, Shah Nazir, Adnan Tahir, " A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow ", Security and Communication Networksvol.

The improvements made in the last couple of decades in the requirements engineering RE processes and methods have witnessed a rapid rise in effectively using diverse machine learning ML techniques to resolve several multifaceted RE issues.

One such machine learning literature issue is the effective identification and classification of the software requirements on Stack Overflow SO for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing NLP techniques.

Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. This paper reports a systematic literature review SLR collecting empirical evidence published up to May This SLR study found 2, published papers related to RE and SO.

The data extraction process of the SLR showed that 1 Latent Dirichlet Allocation LDA topic modeling is among the widely used ML algorithm in the selected studies and 2 precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms, machine learning literature.

The RE activity is steered in the very first phase of software development lifecycle and plays a very pivotal role in ensuring the development of quality and secure software systems [ 12 ]. There are various activities i. Normally software requirements are of two types, namely, FRs and NFRs.

The research work on the difference between FRs and NFRs is defined and well known; however, the automatic identification and classification of the software requirements stated in different natural language is still a huge challenge [ 14 — 21 ]. In addition, some other machine learning literature which make this task problematic are the diversity of stakeholders, difference in the terminologies used, structures of the sentences, and the language used to specify the same kind of requirements [ 1424 ].

Nonetheless, a complete, systematic, and detailed comprehension of these emerging ML based techniques for identification or recognition of software requirements on SO is currently unavailable in the existing literature [ 13 ]. For this SLR study, we have thoroughly followed the systematic literature review SLR machine learning literature as our primary research method [ 2526 ], with the aim of identifying and classifying the available empirical evidence about the use of emerging ML methods or algorithms for diverse software requirements identification on the SO platform for developing quality systems.

The SLR method has been used successfully on diverse topics within the area of requirements engineering [ 27 — 30 ]. This work presented a detailed SLR work of 12 primary studies related to the emerging ML based approaches or techniques for software requirements recognition or identification on the SO platform. The main research goal of this SLR study is to recognize or identify and categorize or classify the type of machine learning algorithms or techniques used for identifying software requirements on the Stack Overflow platform.

Our SLR work is aimed at identifying various types of machine learning algorithms or techniques that have been properly utilized to identify the diverse software requirements on the SO, their working, and evaluation mechanisms. These outcomes will ultimately help and enable us to recognize the main complex issues and challenges machine learning literature need to be properly tackled to enhance the working capabilities of the different machine learning based techniques.

The specific contributions of our SLR study worth mentioning are as follows. The goal of our SLR work is broad enough so we divided it into the set of different research questions RQs specified as follows. RQ1: what types of software requirements identified or reported in the selected studies? RQ2: what are the types of ML algorithms that have been used for identifying software requirements on SO in the selected studies?

Do the ML based approaches outperform the non-ML based approaches? Are there any ML based techniques that considerably outperform the other ML based techniques? RQ3: what are the types of procedures the machine learning literature machine learning algorithms use to identify software requirements on SO?

RQ4: what are the methods utilized to assess the performance of the machine learning algorithms applied in the selected studies?

What are the performance outcomes of the reported ML algorithms? The remainder of the research paper is organized as follows: Section 2 basically describes all the related work. The machine learning literature methodology followed for this study is explained comprehensively in Section 3.

In Section 4we briefly presented the main results and discussion of the SLR study, machine learning literature. Section 5 briefly summarizes the key findings, machine learning literature, limitations, and machine learning literature challenges identified in our SLR study, machine learning literature.

The different types of the validity threats of machine learning literature SLR study are discussed in detail in Section 6. Finally, Section 7 concludes the paper and discusses briefly how the key findings of our SLR study can be further effectively used by the researchers and practitioners in their future research endeavors.

InAhmad et al. It also empowers software programmers to utilize such platforms for the recognized underutilized different tasks of software development lifecycle, e. Similarly, the work of Baltadzhieva and Chrupała [ 31 ] thoroughly reviewed and analyzed various questions quality posted on diverse community question answering CQA websites like SO, machine learning literature.

Besides, they pointed out the different metrics through which the quality of the posted questions can be identified, which in large ultimately lead to affecting the question quality. InMeth et al. They selected 36 primary papers published machine learning literature January and March They categorized the identified works through an analysis framework, including tool categorization, technological concepts, and assessment approaches.

Later on, machine learning literature, Binkhonain and Zaho [ 33 ] conducted an SLR on ML algorithms for identifying and classifying NFRs. They have selected 24 primary studies published.

The key findings of their work revealed that ML approaches could identify and classify NFRs, but they still have many challenges that need more attention. Recently, Iqbal et al. The results revealed that the impact of ML algorithms could be found in different phases of RE lifecycle, e, machine learning literature. Unlike the different related works cited above, our study is focused on identifying and classifying ML algorithms or techniques used for identifying diverse software requirements on the SO only.

We have selected 12 primary studies for our SLR work published until May Thus, we are the first to perform a comprehensive SLR study aimed at identifying, reviewing, summarizing, assessing, and thoroughly reporting the diverse works of ML algorithms or machine learning literature for identifying diverse software requirements on the SO.

There has been a rapid surge in the popularity of using the Evidence-Based Software Engineering EBSE among researchers due to the applicability of systematic literature review SLR in various domains [ 41 — 43 ]. The key goal of the SLR study is to machine learning literature identify, classify, and thoroughly synthesize any new evidence based on the data extracted from the selected research publications. We conducted a comprehensive SLR study, thoroughly following the systematic guidelines defined and stated in [ 25machine learning literature, 2644 — 46 ], with the aim of identifying and classifying the types of machine learning algorithms or techniques used for identifying the software requirements on the Stack Overflow, machine learning literature.

In addition, we have also added snowballing search strategy [ 48 ] as a complementary strategy in addition to the automated electronic data sources with the aim of not overlooking any relevant paper. Basically, the conference paper just reported the initial findings with no detailed analysis of the results. In this work we have deeply assessed all the findings of the research questions, reported the key findings and limitations, and discussed the open challenges for future researchers. The subsequent subsections give comprehensive information regarding the main activities of the SLR protocol used.

The key research goal of our SLR study is to recognize or identify and classify or categorize the different types of machine learning algorithms or techniques used for identifying the software requirements on the Stack Overflow. The goal of our SLR work is broad enough, machine learning literature, so we divided it into the set of different research questions RQs specified in Table 1.

We also collected evidence to answer some interesting demographic questions DQs as suggested in [ 44 — 46 ] associated with the identification of the most actively participating researchers, organizations affiliations academia or industryand countries, as well as the top publication venues.

Table 2 presents briefly the description of every DQ. The EBSE technique is totally dependent on the approach of identifying, machine learning literature, collecting, and summarizing all the existing empirical evidence. Nonetheless, it is quite hard to fully ensure that all the existing empirical evidence was recognized; we ultimately need to machine learning literature the validity threat of not relying on single search strategy [ 49 ].

Therefore, two diverse search approaches, i. The relevant experts on the RE and Stack Overflow areas validated the diverse search strategies. The search was thoroughly performed in four diverse electronic data sources EDSnamely, the ACM Digital Library, IEEE Xplore, Scopus, and Web of Science WoSrespectively [ 5051 ]. These different EDS ensure including the diverse main venues i. The automated search strings were developed from the combination of the key terms extracted from our defined research questions, keywords from the different research publications retrieved by a pilot search, and the list of synonyms.

We conducted several search machine learning literature in the diverse EDS until we accomplished the best balance between precision and recall measures. Table 3 presents the set of final selected search strings, adapted to each of the four electronic data sources, machine learning literature, respectively. To start the snowballing process, the 12 primary studies were used as initial seeds that were selected from the automated search strategy.

In the snowballing process, it is vital machine learning literature consider only the suitable or relevant research studies, so to ensure this, we adopted a top-down sequential process to include only the relevant research papers for each stage of the new iteration. To guarantee the relevance, we also performed the data extraction with the aim that the preselected papers were suitable for answering the defined research questions.

All those papers were selected as the new seeds for the next stage or iteration of the snowballing process which passed the data extraction criterion. Finally, the snowballing process retrieved 1, machine learning literature and ended machine learning literature process at the third iteration with no new primary papers found. To minimize the possible biasness, two of the authors individually conducted the selection of the papers, and a third author carefully reviewed the data generated from every iteration, integrated the individual outcomes, and thoroughly assessed them for any disagreements.

Tables 4 and 5machine learning literature, respectively, present the outputs of the two adopted search approaches for the SLR study. The selection process primarily consists of two tasks: a principally perfect definition of both inclusion and exclusion criteria and truly applying these definite benchmarks to select the pertinent primary research studies [ 5556 ].

As inclusion and exclusion are principally two conflicting activities, we chose to categorically focus our efforts on the exclusion criterion, by outlining a clear set of criteria, both objectively and subjectively appropriate. The former one does not cause any sort of threat to the validity, and, henceforth, its application is much easier and simpler.

While applying the very first exclusion criterion, specifically, those related to the language and duplicity assisted us to remove irrelevant data quite rapidly. For this SLR study, the following objective exclusion criteria were applied to all the retrieved papers: a Exclusion criterion 1: research papers not written in English language b Exclusion criterion 2: short research papers less than four pages in length c Exclusion criterion 3: research papers not published in peer-reviewed publication venues d Exclusion criterion 4: research papers that are not a primary research study secondary and tertiary research studies e Exclusion criterion 5: any kind of grey literature books, presentations, machine learning literature, poster sessions, forewords, talks, editorials, tutorials, panels, machine learning literature, etc.

f Exclusion criterion 6: all sorts of research thesis whether Ph. or master or bachelor theses, machine learning literature. It is obvious that subjective criteria are very complex to address adequately in any SLR study including this one. They are prone to create biasness into the SLR study, and, thus, a predefined protocol principally needs to be applied with the aim of minimizing this threat.

On the contrary, applying these criteria might also leads to a substantial reduction in the number of research papers to machine learning literature as being relevant. For this SLR study, the authors applied the two exclusion criteria described as follows: a Not focus: research studies not related to any of the Machine learning literature activities on the Stack Overflow b Out of scope: research studies not related to any of the RE phases of software development lifecycle.

Any research paper not excluded by the aforementioned criteria was deemed relevant and included in the set of final selected primary research studies. The authors primarily adopted a top-down method to the application of these criteria on the research papers, machine learning literature.

In the first stage, some metadata information such as the title, abstract, and keywords of the research paper was taken into consideration. If these data were not sufficient to exclude any research paper at hand, then the authors machine learning literature the full text of the research publication, more specifically the introduction problems and contributions of the research studythe results, and conclusions sections of the research study. To handle appropriately with any disagreements, the authors primarily followed the inclusive criteria as systematically suggested in [ 44 ] and described in detail in Table 6.

The complete diagrammatic flow of both the searches performed EDS and Snowballing machine learning literature, detailed systematic selection processes and the outcome of every task of the SLR study are reflected in Figure 1. A final set of 12 research papers was selected for this SLR study the detailed list with full bibliographic references is provided in Table 7. Besides, the details of the quality assessment criterion adopted for the SLR study are described in the next section.

For any research publication to pass the defined selection phase, a comprehensive quality assessment criterion was defined, machine learning literature. The DEF see Table 9 was mainly used to extract and store the data for each of the selected research studies.

Machine Learning Basics - What Is Machine Learning? - Introduction To Machine Learning - Simplilearn

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literature review of machine le arning techniques to analyse FLIGHT DATA S Kumar Jasra 1, J Gauci 1, A Muscat 2, G Valentino 3, D Zammit-Mangion 1 and R Camill eri 1 This SLR revealed that machine learning algorithms, specifically SVM (SL algorithm), possess great potential in the RE domain, as they proved to have considerably better performance, LDA (USL algorithm) is the most widely used machine learning algorithm, and the precision and recall are amongst the commonly utilized evaluation methods to measure the performance of these machine learning Cited by: 2 and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational blogger.com Size: 1MB