What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Software
Martin Shepperd mostly deals with Software, Data mining, Software metric, Software quality and Artificial intelligence.
His study in the field of Empirical process, COCOMO and Software development effort estimation also crosses realms of Context.
His research in Data mining intersects with topics in Software bug, Project management and Missing data.
His Software metric study is concerned with Software development in general.
The study incorporates disciplines such as Quality, Data quality, Analytic hierarchy process, Project manager and Empirical research in addition to Software quality.
His study explores the link between Artificial intelligence and topics such as Machine learning that cross with problems in Hill climbing and Heuristics.
His most cited work include:
- Estimating software project effort using analogies (858 citations)
- A Systematic Review of Software Development Cost Estimation Studies (738 citations)
- What accuracy statistics really measure (360 citations)
What are the main themes of his work throughout his whole career to date?
Software, Artificial intelligence, Data mining, Software metric and Machine learning are his primary areas of study.
His Software study integrates concerns from other disciplines, such as Cost estimate, Estimation, Data set and Empirical research.
His study on Case-based reasoning and Variety is often connected to Systematic review as part of broader study in Artificial intelligence.
As part of one scientific family, Martin Shepperd deals mainly with the area of Data mining, narrowing it down to issues related to the Software quality, and often Data quality.
Software metric is a primary field of his research addressed under Software development.
The various areas that Martin Shepperd examines in his Machine learning study include Classifier, Software bug, Quality and Training set.
He most often published in these fields:
- Software (40.99%)
- Artificial intelligence (26.09%)
- Data mining (22.98%)
What were the highlights of his more recent work (between 2013-2021)?
- Context (14.29%)
- Artificial intelligence (26.09%)
- Machine learning (20.50%)
In recent papers he was focusing on the following fields of study:
Martin Shepperd spends much of his time researching Context, Artificial intelligence, Machine learning, Software and Software bug.
His biological study spans a wide range of topics, including Aggregate and Statistical significance.
The Machine learning study combines topics in areas such as Classifier, Empirical research, Small data sets and Mechanism.
His work carried out in the field of Software brings together such families of science as Feature and Data mining.
He interconnects Component-based software engineering, Estimation and Data set in the investigation of issues within Data mining.
His Software bug study combines topics from a wide range of disciplines, such as Confusion matrix, Benchmark and Word error rate.
Between 2013 and 2021, his most popular works were:
- Researcher Bias: The Use of Machine Learning in Software Defect Prediction (193 citations)
- A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction (63 citations)
- Four commentaries on the use of students and professionals in empirical software engineering experiments (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of investigation include Artificial intelligence, Machine learning, Context, Software and Software bug.
His work deals with themes such as Empirical research and Data mining, which intersect with Software.
His Empirical research study combines topics in areas such as Data modeling and Component-based software engineering.
His Data mining research includes elements of Estimation and Data set.
In his study, Training set is inextricably linked to Classifier, which falls within the broad field of Software bug.
The concepts of his Agile software development study are interwoven with issues in Test-driven development, Quality, Baseline, Software quality and Operations research.
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