Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Software Engineers for Qualitative Evaluation of Software
  Architecture   A software architect uses quality requirements to design the architecture of
a system. However, it is essential to ensure that the system's final
architectural design achieves the standard quality requirements. The existing
architectural evaluation frameworks require basic skills and experience for
practical usage, which novice software architects lack.
  We propose a framework that enables novice software architects to infer the
system's quality requirements and tactics using the software architectural
block-line diagram. The framework takes an image as input, extracts various
components and connections, and maps them to viable architectural patterns,
followed by identifying the system's corresponding quality attributes (QAs) and
tactics. The framework includes a specifically trained machine learning model
based on image processing and semantic similarity methods to assist software
architects in evaluating a given design by a) evaluating an input architectural
design based on the architectural patterns present in it, b) lists out the
strengths and weaknesses of the design in terms of QAs, c) recommends the
necessary architectural tactics that can be embedded in the design to achieve
the lacking QAs.
  To train our framework, we developed a dataset of 2,035 architectural images
from fourteen architectural patterns such as Client-Server, Microservices, and
Model View Controller, available at
https://www.doi.org/10.6084/m9.figshare.14156408. The framework achieves a
Correct Recognition Rate of 98.71% in identifying the architectural patterns.
We evaluated the proposed framework's effectiveness and usefulness by using
controlled and experimental groups, in which the experimental group performed
approximately 150% better than the controlled group. The experiments were
performed as a part of the Masters of Computer Science course in an Engineering
Institution.

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The categories of this paper are: eess
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
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The categories of this paper are: stat
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con