A Query Language for Software Architecture Information (Extended
version)
- URL: http://arxiv.org/abs/2306.16829v2
- Date: Tue, 4 Jul 2023 11:46:13 GMT
- Title: A Query Language for Software Architecture Information (Extended
version)
- Authors: Joshua Ammermann, Sven Jordan, Lukas Linsbauer, Ina Schaefer
- Abstract summary: Maintenance tasks of existing software systems suffer from architecture information diverging over time.
The Digital Architecture Twin (DArT) can support software maintenance by providing up-to-date architecture information.
We contribute the Architecture Information Query Language (AIQL) which enables stakeholders to access up-to-date and tailored architecture information.
- Score: 3.348168323147728
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software maintenance is an important part of a software system's life cycle.
Maintenance tasks of existing software systems suffer from architecture
information that is diverging over time (architectural drift). The Digital
Architecture Twin (DArT) can support software maintenance by providing
up-to-date architecture information. For this, the DArT gathers such
information and co-evolves with a software system, enabling continuous reverse
engineering. But the crucial link for stakeholders to retrieve this information
is missing. To fill this gap, we contribute the Architecture Information Query
Language (AIQL), which enables stakeholders to access up-to-date and tailored
architecture information. We derived four application scenarios in the context
of continuous reverse engineering. We showed that the AIQL provides the
required functionality to formulate queries for the application scenarios and
that the language scales for use with real-world software systems. In a user
study, stakeholders agreed that the language is easy to understand and assessed
its value to the specific stakeholder for the application scenarios.
Related papers
- Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction [43.045596895389345]
We introduce a modern, semi-automated approach for creating datasets, encompassing tasks such as KBQA, Machine Reading (MRC), and Information Retrieval (IR)
We provide a comprehensive implementation, insightful findings, detailed statistics, and evaluation of baseline models.
arXiv Detail & Related papers (2024-08-05T09:23:49Z) - Software Architecture Recovery with Information Fusion [14.537490019685384]
We propose SARIF, a fully automated architecture recovery technique.
It incorporates three types of comprehensive information, including dependencies, code text and folder structure.
SARIF is 36.1% more accurate than the best of the previous techniques on average.
arXiv Detail & Related papers (2023-11-08T12:35:37Z) - Serving Deep Learning Model in Relational Databases [70.53282490832189]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks.
The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - Architecture Knowledge Representation and Communication Industry Survey [0.0]
We aim to understand the current practice in architecture knowledge, and to explore where sustainability can be applied to address sustainability in software architecture in the future.
We used a survey, which utilized a questionnaire containing 34 questions and collected responses from 45 architects working at a prominent bank in the Netherlands.
arXiv Detail & Related papers (2023-09-20T18:17:16Z) - Machine Learning-Enabled Software and System Architecture Frameworks [48.87872564630711]
The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.
We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
arXiv Detail & Related papers (2023-08-09T21:54:34Z) - Towards Human-Bot Collaborative Software Architecting with ChatGPT [7.50312929275194]
Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support.
ChatGPT is a disruptive technology not primarily introduced for software engineering.
We detail a case study that involves collaboration between a novice software architect and ChatGPT for architectural analysis, synthesis, and evaluation of a services-driven software application.
arXiv Detail & Related papers (2023-02-26T16:32:16Z) - Retrieval-Enhanced Machine Learning [110.5237983180089]
We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases.
REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization.
REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
arXiv Detail & Related papers (2022-05-02T21:42:45Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.