With software systems becoming increasingly pervasive and autonomous, our
ability to test for their quality is severely challenged. Many systems are
called to operate in uncertain and highly-changing environment, not rarely
required to make intelligent decisions by themselves. This easily results in an
intractable state space to explore at testing time. The state-of-the-art
techniques try to keep the pace, e.g., by augmenting the tester's intuition
with some form of (explicit or implicit) learning from observations to search
this space efficiently. For instance, they exploit historical data to drive the
search (e.g., ML-driven testing) or the tests execution data itself (e.g.,
adaptive or search-based testing). Despite the indubitable advances, the need
for smartening the search in such a huge space keeps to be pressing.
We introduce Reasoning-Based Software Testing (RBST), a new way of thinking
at the testing problem as a causal reasoning task. Compared to mere
intuition-based or state-of-the-art learning-based strategies, we claim that
causal reasoning more naturally emulates the process that a human would do to
''smartly" search the space. RBST aims to mimic and amplify, with the power of
computation, this ability. The conceptual leap can pave the ground to a new
trend of techniques, which can be variously instantiated from the proposed
framework, by exploiting the numerous tools for causal discovery and inference.
Preliminary results reported in this paper are promising.

RBST 提出一种新的软件测试思路，即将测试问题视为因果推理任务，通过利用因果推理的工具来智能化测试搜索过程，与基于经验学习的策略相比具有更高的自然性和灵活性，其初步结果是有前途的。