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SEMAT: Applications & Technology
SEMAT (Software Engineering Method and Theory) is an initiative to reshape software engineering such that software engineering qualifies as a rigorous discipline. The initiative was launched in December 2009 by Ivar Jacobson, Bertrand Meyer, and Richard Soley with a call for action statement and a vision statement. The initiative was envisioned as a…
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SEMAT topic overview
The analysis highlights Applications and Technology as prominent areas in the source structure around SEMAT.
Related topics
18
Source areas
5
Connected nodes
23
Extracted relationships
107
Related term clusters
10
Bridge connections
23
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Organizational structure · 10 topics
Overview · 4 topics
Practical Applications of SEMAT · 2 topics
Practice area · 1 topics
Tools supporting SEMAT · 1 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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SEMAT
4Software engineering · Ivar Jacobson · Bertrand Meyer
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
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Advanced semantic analysis
How SEMAT connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around SEMAT shows recurring relationship patterns in the source. For example, SEMAT → Argentina, August, Brazil, Carlos Zapata, CCC, Chapter, Chile, Chilean Computing Meeting, CLEI, Colombia, Colombian Computing Conference, Dr, ECC, Essence, Executive Committee, Ibero American Software Engineering, Informatics, Ivar Jacobson, JIISIC, Knowledge Engineering Journeys Another extracted example is SEMAT → Actual Problems, EC-leasing, Economics, Essence, Higher School, INCOSE Russian Chapter, Informatics, Kernel, Moscow Institute, Moscow State University, Physics, Russian, Russian Chapter, SECR, Software Engineering, Software Life Cycle, Statistics, System, Technology, Translation. Use these groups to spot repeated connection types before inspecting the individual relationships.
SEMAT → Actual Problems, EC-leasing, Economics, Essence, Higher School, INCOSE Russian Chapter, Informatics, Kernel, Moscow Institute, Moscow State University, Physics, Russian, Russian Chapter, SECR, Software Engineering, Software Life Cycle, Statistics, System, Technology, Translation
related to Main organization· 12
SEMAT → Cecile Peraire, Fujitsu, Ivar Jacobson, Malhotra, McMahon, Michael Goedicke, Paul, Ste Nadin, Sumeet, Tata Consultancy Services, The CEO, The Executive Management Committee
related to Tools supporting SEMAT· 12
SEMAT → Essence KernelPractice, Essence LanguageMethod, EssWork, EssWork Practice Workbench, HTML, Integrated Practice Development Environment, Interactive, Ivar Jacobson International, June, Key, Practice Workbench, The Practice Workbench
related to Japan Chapter· 7
SEMAT → April, BoKs, Japan Chapter, Japanese, Member, November, SEMAT Essence
has application· 5
SEMAT → Four, Ideas, Munich Re, Notable, Reinsurance
related to Theory area· 4
SEMAT → General Theory, GTSE, SEMAT Workshop, Software Engineering
related to Education area· 1
SEMAT → Essence
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 107 structured relationships around SEMAT. Examples in this analysis include the preferences of the team using it → instance of → specific for all kinds of reasons and university professors → instance of → The area's target groups are instructors. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
the preferences of the team using it
instance of
specific for all kinds of reasons
0.80
text
kind of software being built
instance of
specific for all kinds of reasons
0.80
text
etc
instance of
specific for all kinds of reasons
0.80
text
university professors
instance of
The area's target groups are instructors
0.80
text
industrial coaches as well as their students
instance of
The area's target groups are instructors
0.80
text
learning practitioners.The goal of the area is to create educational courses
instance of
The area's target groups are instructors
0.80
text
course materials that are internationally viable
instance of
The area's target groups are instructors
0.80
text
identify pedagogical approaches that are appropriate
instance of
The area's target groups are instructors
0.80
text
effective for specific target groups
instance of
The area's target groups are instructors
0.80
text
disseminate experience
instance of
The area's target groups are instructors
0.80
text
lessons learned.The area includes members from a number of universities
instance of
The area's target groups are instructors
0.80
text
institutes worldwide
instance of
The area's target groups are instructors
0.80
text
No triples extracted yet.
Related concept clusters Related term clusters
Related term clusters group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around SEMAT bring nearby vocabulary together. In this analysis, examples include Engineering, Software and Chapter. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
SEMAT
Engineering
Software
Chapter
Theory
Essence
Kernel
General
Citation
Needed
Community
Members
Practice
semat
Engineering
Software
Chapter
Theory
Essence
Kernel
General
Citation
Needed
Community
Members
Practice
software engineering
Software
Semat
Theory
Essence
General
Kernel
Chapter
Methods
Using
Area
Latin
Practices
requirements engineering
Software
Semat
Theory
Essence
General
Chapter
Area
Latin
Citation
Needed
Education
Members
knowledge engineering
Software
Semat
Theory
Essence
General
Chapter
Area
Latin
Citation
Needed
Education
Members
systems engineering
Software
Semat
Theory
Essence
General
Chapter
Area
Latin
Citation
Needed
Education
Members
practice area
Workbench
Education
Area
Practice
Tool
Theory
Would
Work
Practices
Citation
Needed
Practical
practical applications of semat
Engineering
Software
Chapter
Theory
Essence
Practice
Would
Kernel
Latin
Step
General
Citation
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For SEMAT, one of the stronger structural bridges in this analysis connects SEMAT with Organizational structure. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
SEMAT
Nodes24
Edges23
Triples107
Avg. degree1.92
Density0.083333
Components1
Source & methodology
TTTA analyzes the structure around SEMAT to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications & Technology, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — SEMAT · EN edition · Analysis: TopicsToTalkAbout
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