2017 06-01-eswc2017-ug

http://aligned-project.eu ESWC, 1st June 2017, Portoroz
Ontology-Driven Uniﬁed Governance in
Software Engineering
The PoolParty case study
Monika Solanki
https://w3id.org/people/msolanki
@nimonika
University of Oxford
Joint work with
Christian Mader, Helmut Nagy, Margot Mückstein, Mahek Hanﬁ,
Robert David & Andreas Koller

monika.solanki@cs.ox.ac.uk, @nimonika Ontology-Driven Uniﬁed Governance

Motivation
Contemporary Software Development Environments
Transformed the way software is developed.
Collaborative, Dynamic and Agile → Github, JIRA
Complex social activity - teams of software architects,
developers, testers and maintainers
Requirements → Several iterations, deliberations and
informal discussions
Requirements, discussions and implementations issues
are recorded as unstructured text

Collaborative development environments - Limitations
Generic fields such as “issue” and “comment” which
encapsulate all discussion types. This makes it
extremely difficult to retrieve information relevant to a
specific aspect of a requirement.
No provision for interfaces to associate design
requirements with implementation issues in a
structured and systematic way.

Contributions
An ontology-driven approach based on DIO* that captures
the knowledge emerging during software design,
development, implementation and maintenance and
exploits it for uniﬁed governance of the engineering
process.
An industry-driven use case from the Semantic Web
Company → Pool Party Thesaurus server.
A graph search powered, uniﬁed governance dashboard
implementation over the annotated and integrated
datasets.
*https://w3id.org/dio

Motivating scenario - Current setup at SWC
Atlassian Confluence → requirements engineering for
each version of PPT.
JIRA
team members & SWC customers: issue and change
tracking,
team members: organising ideas
Agile methodology: data recorded in Confluence as
“Requirements”, “Goal”,“User Story”, “Epic” and
“Stakeholders”.
Additional context: “Precondition”, “Detailed description”,
“Acceptance criteria & Test scenario”.
A single field, “Comment”, captures the
opinions/discussion carried out by human agents.

Design Intent Ontology

Domain specific models
As DIO is a general purpose design intent ontology, it does
not capture attributes specific to PoolParty development.
To bridge this gap, we define DIO-PP - an extension to DIO
that is specifically aimed at capturing conceptualisations
from Confluence and JIRA.
Besides DIO-PP, we also define bespoke mappings to DIO
entities, for the data recorded during requirements capture.

Knowledge Representation Framework

Mapping: Epic → Design Requirements
The Epic captures the high level description of the
requirements.
Epic → dio : DesignRequirement
The epic’s title is mapped as a dc:title for the
dio:DesignRequirement
Epic/title → (dio : DesignRequirement
dc : title xsd : String)
The epic’s goal is mapped as a dc:description for the
dio:DesignRequirement
Epic/goal → (dio : DesignRequirement
dc : description xsd : String)

Mapping: User story → Design Intent
Each Epic includes a set of requirements, each of which are
represented as a “User story”
User story → dio : DesignIntent
The user story’s title is mapped as a dc:title for the
dio:DesignIntent
User story/title → (dio : DesignIntent
dc : title xsd : String)
The user story’s description is mapped as a dc:description
for the dio:DesignIntent
User story/description → (dio : DesignIntent
dc : description xsd : String)

Mapping: User story → DIO-PP
The user story includes part of the solution, that addresses the
requirement.
diopp : Precondition dio : Assumption
diopp : AffectedComponent dio : Heuristic
diopp : AcceptanceCriteria dio : Evaluation
dio : Comment dio : Argument
dio : Justification

Mapping: JIRA design issues
DesignIssue ((= 1 isOfIssueType.IssueType)
(= 1 hasVersionType.VersionType)
(= 1 hasPriority.PriorityType)
(= 1 hasStatus.IssueStatusType)
(= 1 hasResolution.IssueResolution)
(= 1 hasReporter.Agent)
(= 1 hasAssignee.Agent)
(≥ 1 hasAffectedComponent.Component)
(≥ 1 hasWatcher.Agent)
(≥ 1 hasEnvironment. )
(≤ 1 updateDate))

Data Extraction Workflow
Contributions
An extraction tool which connects to the Confluence and
JIRA instances for PPT and converts the contained data
into RDF.
A Data Processing Unit (DPU) for UnifiedViews
A UnifiedViews pipeline, which encompasses data
extraction, data annotation using a PPT thesaurus and
loading the annotated data into a remote Virtuosos triple
store.

Data Extraction Workﬂow

Faceted browsing Dashboard

Full-text Seacrh Dashboard

Evaluation
Six participants (members of the consulting team at SWC)
with a background in software engineering.
All the recruited personnel had reasonable experience in
the use of Confluence, JIRA and ontology interpretation.
Six competency questions - structured data and full-text
search.
Task 1: Searching Confluence and JIRA to retrieve
requirements and answer the corresponding competency
questions.
Task 2: Using the unified governance dashboard to answer
the same competency questions.
All evaluation data including participants strategies, timings and
the resulting timing analysis graphs has been made available at
http://goo.gl/Khlaaf.

Evaluation questions
Question Use case Mapped DIO/DIOPP concept
Which feature caused most bug
reports in release 5.5.0 (af-
fected Version 5.5.0)?
This goes towards release pro-
filing: finding out which features
are weak points of a certain re-
lease.
Bug reports → dio:DesignIssue
Which PoolParty version (start-
ing from PP 2.8 and includ-
ing bug fix, releases in the re-
spective minor or major re-
lease (e.g. count 5.3.1 for,5.3))
was the one affected with the
most bugs that were classified
as,"blocker", and how would you
interpret this?
In order to know if we get bet-
ter in our development and sup-
port,processes, it is useful to
know if we are encountering
more or less,critical issues than
before.
Bug status→ dio:IssueStatusType,
Bug type → dio:IssueType

Evaluation
For each of the tasks and for each question, the following
observations were recorded:
The time taken to answer each question in task 1 and 2.
This was the total time taken for searching and analysing
the results.
The answers for each question in task 1 and 2.
The strategy used to answer the question.

Evaluation

Analysis
Overall time taken to search structured data is 50% less
when compared to the time taken to search Confluence
and JIRA for the same questions.
The evaluation also highlighted the weakness of the full
text search implementation used in the unified governance
dashboard, where for atleast three of the participants, to
search answers to questions 5 and 6 took significantly
longer as compared to using the full text search provided
by Confluence and JIRA.
We were also able to identify pitfalls and bottlenecks in the
current design of the unified governance dashboard as an
additional evaluation result.

Beneﬁts and Impact
Ontologies → create merged repositories of requirements,
customer feedback, bug reports and project documentation
Consolidate PoolParty experiences, customer ideas and market
needs in order to integrate them into products → key factor for
successful development of SWC products and for raising
customer satisfaction and enterprise agility.
Data integration → enable the mining of intents that lead to the
development in PoolParty.
Questions asked by customers ﬂow faster into the requirements
engineering system.

Summary
An ontology-driven framework for integrating the
requirements and issues arising during software design,
implementation and maintenance.
Evaluation on an industrial software engineering case
study for PPT.
An impressive 50% reduction in the time taken to search
semantically annotated and integrated datasets when
compared to the search provided by Conﬂuence and JIRA.
The evaluation also revealed the limitation of the full text
search feature currently implemented in the uniﬁed
governance dashboard.

2017 06-01-eswc2017-ug

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