迈向自动化：SBSE教程——软件工程中的搜索技术与应用

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"基于搜索的软件工程（Search Based Software Engineering，SBSE）是一种前沿的软件开发方法，它将传统的软件工程问题解决策略从依赖人类智慧和经验转变为借助计算机算法的自动化搜索过程。该方法借鉴了元启发式搜索、运筹学和进化计算等领域的技术，旨在通过机器的持久性和可靠性来减轻人为错误和提高工程效率，从而降低整体成本。SBSE的核心思想在于协同人类创新与机器处理大规模数据的能力，以实现更高效的问题求解和决策支持。 SBSE教程的目标是为读者提供一个全面的学习路径，无论他们是否具备搜索优化的专业背景。教程内容覆盖了从基础概念到实际应用的各个环节，包括但不限于搜索技术的分类、方法论和具体实践。通过逐步指导，读者可以学习如何选择合适的搜索算法，如何构建搜索模型，以及如何将这些技术融入到软件开发的各个阶段，如需求分析、设计、测试和维护等。参与教程的作者Mark Harman、Phil McMinn、Jefferson Teixeira de Souza和Shin Yoo分别来自伦敦大学学院、谢菲尔德大学和巴西沙佩尔州立大学，他们的专业背景使得教程内容具有深厚的学术底蕴和实践经验。教程强调了在实践中应用SBSE的重要性，鼓励读者将搜索优化技术应用于自己感兴趣的软件工程问题上，从而提高项目成功率和质量。学习这个教程可以帮助软件工程师提升问题解决能力，减少人工错误，并通过决策支持工具优化软件开发流程，实现更高的生产率和创新能力。无论是对于初学者还是经验丰富的从业者，这都是一份宝贵的资源，有助于他们在软件工程领域内探索基于搜索的新思路和方法。"

Fitness

Space of all possible solutions

Fig. 7. Genetic Algorithms are global searches, taking account of several points in the search space at once

(adapted from McMinn [66])

Randomly generate or seed initial population P

Repeat

Evaluate ﬁtness of each individual in P

Select parents from P according to selection mechanism

Recombine parents to form new oﬀspring

Construct new population P

from parents and oﬀspring

Mutate P

P ← P

Until Stopping Condition Reached

Fig. 8. High level description of a Genetic Algorithm, adapted from McMinn [65]

5 Getting The First Result: A Simple Example for Regression Testing

This section presents an application of a search-based approach to the Test Case Prioritisation

(TCP) in regression testing, illustrating the steps that are necessary to obtain the ﬁrst set of results.

This makes concrete the concepts of representation, ﬁtness function and search based algorithm (and

their operators) introduced in the previous sections. First, let us clarify what we mean by TCP.

Regression testing is a testing activity that is performed to gain conﬁdence that the recent

modiﬁcations to the System Under Test (SUT), e.g. bug patches or new features, did not interfere

with existing functionalities [108]. The simplest way to ensure this is to execute all available tests;

this is often called retest-all method. However, as the software evolves, the test suite grows too,

eventually making it prohibitively expensive to adopt the retest-all approach. Many techniques have

been developed to deal with the cost of regression testing.

Test Case Prioritisation represents a group of techniques that particularly deal with the permu-

tations of tests in regression test suites [28, 108]. The assumption behind these techniques is that,

because of the limited resources, it may not be possible to execute the entire regression test suite.

The intuition behind Test Case Prioritisation techniques is that more important tests should be

executed earlier. In the context of regression testing, the ‘important’ tests are the ones that detect

regression faults. That is, the aim of Test Case Prioritisation is to maximise earlier fault detection

rate. More formally, it is deﬁned as follows:

Deﬁnition 1 Test Case Prioritisation Problem

Given: a test suite, T , the set of permutations of T , P T , and a function from P T to real numbers,

f : P T → R.

Problem: to ﬁnd T

∈ P T such that (∀T

)(T

∈ P T )(T

6= T

)[f(T

) ≥ f(T

)].

Ideally, the function f should be a mapping from tests to their fault detection capability. How-

ever, whether a test detects some faults or not is only known after its execution. In practice, a

function f that is a surrogate to the fault detection capability of tests is used. Structural coverage

is one of the most popular choices: the permutation of tests that achieves structural coverage as

early as possible is thought to maximise the chance of early fault detection.

5.1 Representation

At its core, TCP as a search problem is an optimisation in a permutation space similar to the

Travelling Salesman Problem (TSP), for which many advanced representation schemes have been

developed. Here we will focus on the most basic form of representation. The set of all possible

candidate solutions is the set of all possible permutations of tests in the regression test suite. If

the regression test suite contains n tests, the representation takes the form of a vector with n

elements. For example, Figure 9 shows one possible candidate solution for TCP with size n, i.e.

with a regression test suite that contains 6 tests, {t

, . . . , t

Depending on the choice of the search algorithm, the next step is either to deﬁne the neighbouring

solutions of a given solution (local search) or to deﬁne the genetic operators (genetic algorithm).

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