Software metrics for fault prediction using machine learning approaches: A literature review with PROMISE repository dataset

2017 ◽  
Author(s):  
Meiliana ◽  
Syaeful Karim ◽  
Harco Leslie Hendric Spits Warnars ◽  
Ford Lumban Gaol ◽  
Edi Abdurachman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Software Metrics ◽  
Fault Prediction ◽  
Learning Approaches

scholarly journals Performance Evaluation of Pseudo Code with Weka for Accuracy Calculation

2019 ◽  
Vol 8 (4) ◽  
pp. 7818-7823
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Data Transmission ◽  
Software Metrics ◽  
Fault Prediction ◽  
Learning Methods ◽  
Accuracy Measurement ◽  
Machine Learning Methods ◽  
Interoperability Test

Programming testing is a fundamental and essential advance of the existence cycle of programming improvement to recognize and defects in programming and afterward fix the deficiencies. The reliability of the data transmission or the quality of proper processing ,maintenance and retrieval of information to a server can be tested for some systems. Accuracy is also one factor that is usually used to the Joint Interoperability Test Command as a criterion for accessing interoperability. This is the main investigation of PC flaw forecast and exactness as per our examination, which spotlights on the utilization of PROMISE database dataset. Some PROMISE database dataset tests are compared between pseudo code (PYTHON) and actual software (WEKA),which in computer fault prediction and accuracy measurement are effective software metrics and machine learning methods.

scholarly journals A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches

2021 ◽  
pp. 183-204
Author(s):  
Puneeth B. R. ◽  
Nethravathi P. S.
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Research Centre ◽  
Learning Approaches ◽  
Study Objective ◽  
Survey Paper ◽  
Multiple Data ◽  
New Ideas

Background/Purpose: Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the work's credibility. It also improves with the subject's integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study. Objective: The main objective of this literature survey is to explore the different techniques applied to identify and classify the various diseases on arecanut. This paper also recommends the methodology and techniques that can be used to achieve the objectives of the study. Design/Methodology/Approach: Multiple data sources, such as journals, conference proceedings, books, and research papers published in reputable journals, were used to compile the essential literature on the chosen topic and collect information from the arecanuts research centre and many farmers in the south Canara and Udupi districts, before narrowing down the literature that is relevant to the research work. The shortlisted literature was carefully assessed by reading each paper and taking notes as appropriate. The information gathered is then examined to identify the potential gap in the study. Findings/Result: Based on the analysis of the papers reviewed, discussion with farmers and research center officers, it is observed that, not much work is carried out in the field of disease identification and classification on arecanut using machine learning techniques. This survey paper recommends techniques and the methodology that can be applied to identify and classify the diseases in arecanut and to classify them in to healthy and unhealthy. Research limitations/implications: The literature review mentioned in this paper are detection and classification of different diseases in arecanut. Originality/Value: This paper focuses on various online research journals, conference papers, technical books, and web articles. Paper Type: Literature review paper on techniques and methods used to achieve the objectives.

Code Clone Detection Using Machine Learning Techniques

2020 ◽  
Vol 11 (2) ◽  
pp. 49-75
Author(s):  
Amandeep Kaur ◽  
Sandeep Sharma ◽  
Munish Saini
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Clone Detection ◽  
Learning Approaches ◽  
Code Clone ◽  
Online Databases ◽  
Learning Techniques ◽  
Tools And Techniques

Code clone refers to code snippets that are copied and pasted with or without modifications. In recent years, traditional approaches for clone detection combine with other domains for better detection of a clone. This paper discusses the systematic literature review of machine learning techniques used in code clone detection. This study provides insights into various tools and techniques developed for clone detection by implementing machine learning approaches and how effectively those tools and techniques to identify clones. The authors perform a systematic literature review on studies selected from popular computer science-related digital online databases from January 2004 to January 2020. The software system and datasets used for analyzing tools and techniques are mentioned. A neural network machine learning technique is primarily used for the identification of the clone. Clone detection based on a program dependency graph must be explored in the future because it carries semantic information of code fragments.

Overview of Machine Learning Approaches

2017 ◽  
pp. 19-33
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Case Based Reasoning ◽  
Learning Approaches ◽  
Software Module ◽  
Learning Techniques ◽  
Case Based

This chapter enlists and presents an overview of various machine learning approaches. It also explains the machine learning techniques used in the area of software engineering domain especially case-based reasoning method. Case-based reasoning is used to predict software quality of the system by examining a software module and predicting whether it is faulty or non-faulty. In this chapter an attempt has been made to propose a model with the help of previous data which is used for prediction. In this chapter, how machine learning technique such as case-based reasoning has been used for error estimation or fault prediction. Apart from case-based reasoning, some other types of learning methods have been discussed in detail.

Software fault prediction using machine learning techniques with metric thresholds

2021 ◽  
Vol 25 (2) ◽  
pp. 159-172
Author(s):  
Raed Shatnawi
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Prediction Models ◽  
Object Oriented ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Threshold Values ◽  
Data Imbalance ◽  
Learning Techniques

BACKGROUND: Fault data is vital to predicting the fault-proneness in large systems. Predicting faulty classes helps in allocating the appropriate testing resources for future releases. However, current fault data face challenges such as unlabeled instances and data imbalance. These challenges degrade the performance of the prediction models. Data imbalance happens because the majority of classes are labeled as not faulty whereas the minority of classes are labeled as faulty. AIM: The research proposes to improve fault prediction using software metrics in combination with threshold values. Statistical techniques are proposed to improve the quality of the datasets and therefore the quality of the fault prediction. METHOD: Threshold values of object-oriented metrics are used to label classes as faulty to improve the fault prediction models The resulting datasets are used to build prediction models using five machine learning techniques. The use of threshold values is validated on ten large object-oriented systems. RESULTS: The models are built for the datasets with and without the use of thresholds. The combination of thresholds with machine learning has improved the fault prediction models significantly for the five classifiers. CONCLUSION: Threshold values can be used to label software classes as fault-prone and can be used to improve machine learners in predicting the fault-prone classes.

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