Requirements form the basis for defining software systems' obligations and
tasks. Testable requirements help prevent failures, reduce maintenance costs,
and make it easier to perform acceptance tests. However, despite the importance
of measuring and quantifying requirements testability, no automatic approach
for measuring requirements testability has been proposed based on the
requirements smells, which are at odds with the requirements testability. This
paper presents a mathematical model to evaluate and rank the natural language
requirements testability based on an extensive set of nine requirements smells,
detected automatically, and acceptance test efforts determined by requirement
length and its application domain. Most of the smells stem from uncountable
adjectives, context-sensitive, and ambiguous words. A comprehensive dictionary
is required to detect such words. We offer a neural word-embedding technique to
generate such a dictionary automatically. Using the dictionary, we could
automatically detect Polysemy smell (domain-specific ambiguity) for the first
time in 10 application domains. Our empirical study on nearly 1000 software
requirements from six well-known industrial and academic projects demonstrates
that the proposed smell detection approach outperforms Smella, a
state-of-the-art tool, in detecting requirements smells. The precision and
recall of smell detection are improved with an average of 0.03 and 0.33,
respectively, compared to the state-of-the-art. The proposed requirement
testability model measures the testability of 985 requirements with a mean
absolute error of 0.12 and a mean squared error of 0.03, demonstrating the
model's potential for practical use.

本文提出了一种基于需求异味嗅探和自动生成词典的数学模型，对自然语言需求的可测试性进行评估和排名，并通过经验研究验证了该模型在检测需求异味和测量需求测试性方面的卓越性能。

基于需求异味的自然语言需求可测试性衡量

Natural Language Requirements Testability Measurement Based on  Requirement Smells

As the demand for verifiability and testability of neural networks continues
to rise, an increasing number of methods for generating test sets are being
developed. However, each of these techniques tends to emphasize specific
testing aspects and can be quite time-consuming. A straightforward solution to
mitigate this issue is to transfer test sets between some benchmarked models
and a new model under test, based on a desirable property one wishes to
transfer. This paper introduces GIST (Generated Inputs Sets Transferability), a
novel approach for the efficient transfer of test sets among Deep Learning
models. Given a property of interest that a user wishes to transfer (e.g.,
coverage criterion), GIST enables the selection of good test sets from the
point of view of this property among available ones from a benchmark. We
empirically evaluate GIST on fault types coverage property with two modalities
and different test set generation procedures to demonstrate the approach's
feasibility. Experimental results show that GIST can select an effective test
set for the given property to transfer it to the model under test. Our results
suggest that GIST could be applied to transfer other properties and could
generalize to different test sets' generation procedures and modalities

该研究介绍了一种名为 GIST 的新方法，用于在深度学习模型之间有效传递测试集，以迁移用户所需的某个属性，如覆盖准则。实验证明 GIST 能够选择有效的测试集，并将其传递到待测试的模型中。结果表明，GIST 可应用于传递其他属性，并可适用于不同的测试集生成程序和模式。

深度学习中生成输入集的可转移性

GIST: Generated Inputs Sets Transferability in Deep Learning

This paper reviews recent advances in missing data research using graphical
models to represent multivariate dependencies. We first examine the limitations
of traditional frameworks from three different perspectives:
\textit{transparency, estimability and testability}. We then show how
procedures based on graphical models can overcome these limitations and provide
meaningful performance guarantees even when data are Missing Not At Random
(MNAR). In particular, we identify conditions that guarantee consistent
estimation in broad categories of missing data problems, and derive procedures
for implementing this estimation. Finally we derive testable implications for
missing data models in both MAR (Missing At Random) and MNAR categories.

本文从透明度、可估计性和可测试性的三个方面，评估了传统框架的限制，并展示了如何通过基于图形模型的方法来克服这些限制并在包括 MNAR 在内的广泛缺失数据问题中提供有意义的性能保证。同时，提出了在 MAR 和 MNAR 类别中对缺失数据模型进行可测试的推论。