What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Software engineering
- Machine learning
His primary areas of investigation include Decision support system, Software release life cycle, Artificial intelligence, Systems engineering and Resource.
His studies deal with areas such as Influence diagram, Software engineering, Software development and Management science as well as Decision support system.
The concepts of his Software release life cycle study are interwoven with issues in Release management, Software evolution, Software peer review, Process and Social software engineering.
His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Data mining and Topic model.
His Systems engineering study combines topics from a wide range of disciplines, such as Software system and Risk analysis.
His Resource research is multidisciplinary, relying on both Process management and Incremental build model.
His most cited work include:
- The art and science of software release planning (224 citations)
- A flexible method for software effort estimation by analogy (135 citations)
- The Cognitive Process of Decision Making (132 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Decision support system, Process, Software engineering, Software development and Software release life cycle.
While the research belongs to areas of Decision support system, he spends his time largely on the problem of Software system, intersecting his research to questions surrounding Risk analysis.
His work on Requirements engineering as part of general Process research is often related to Set, thus linking different fields of science.
He combines subjects such as Software project management, Software metric, Software construction and Software maintenance with his study of Software engineering.
The study incorporates disciplines such as Task and Process management in addition to Software development.
His Software release life cycle research integrates issues from Release management, Resource and Incremental build model.
He most often published in these fields:
- Decision support system (25.75%)
- Process (20.96%)
- Software engineering (17.96%)
What were the highlights of his more recent work (between 2018-2021)?
- Process (20.96%)
- Empirical research (9.58%)
- Requirements engineering (13.17%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Process, Empirical research, Requirements engineering, Quality and Machine learning.
His Process research is multidisciplinary, incorporating perspectives in Range, Relation, Software engineering and Process management.
His work in Process management addresses issues such as Agile software development, which are connected to fields such as Software development.
His Machine learning study combines topics in areas such as Latent semantic analysis, Probabilistic logic and Artificial intelligence.
His Naive Bayes classifier study frequently draws connections between related disciplines such as Decision support system.
Guenther Ruhe has included themes like Software bug and Data mining in his Software release life cycle study.
Between 2018 and 2021, his most popular works were:
- Status Quo in Requirements Engineering: A Theory and a Global Family of Surveys (30 citations)
- A longitudinal study of identifying and paying down architecture debt (10 citations)
- Data-driven requirements engineering: an update (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Software engineering
- Machine learning
Guenther Ruhe spends much of his time researching Requirements engineering, Identification, Quality, Data-driven and Machine learning.
The subject of his Requirements engineering research is within the realm of Process.
His Identification study integrates concerns from other disciplines, such as Feature, Document classification, Software requirements, Social media and Data science.
His work carried out in the field of Quality brings together such families of science as Optimization problem, Mathematical optimization and Heuristics.
His Data-driven study incorporates themes from Naive Bayes classifier, Support vector machine, Decision support system, Supervised learning and Use case.
His study in Machine learning is interdisciplinary in nature, drawing from both Software development, Systems development life cycle and Latent semantic analysis, Probabilistic logic, Artificial intelligence.
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