The Birth, Life and Death of Semantic Mutants

传统的突变测试产生的测试用例可以区分一些描述N和它的变种。每个变体都是通过对N应用突变操作符来产生的。善于将N与N的变体区分开来的测试集很可能擅长于发现类似于应用突变操作符的错误。突变测试最初应用于程序，但最近已应用于其他形式的描述，如规范。突变是通过应用突变操作符来产生的，每个突变操作符都可以应用于程序中的相关点以产生突变。变异操作符执行微小的句法改变。例如，+可能替换为-，&gt；可能替换为&gt；=，表达式中的变量可能替换为常量，或者表达式的一部分可能被删除。变异操作符被设计来表示语法上的小错误。通常，突变体用于判断测试集的充分性(它是否区分N及其突变体？)传统的突变测试产生的突变代表编程中的小失误或错误，从而代表一类错误。突变程序与被测程序的不同之处在于有一个很小的语法变化(例如，a/替换了a*)。然而，真正的开发人员也会受到误解，特别是在描述符号之间移动时。他们误解了他们面前描述的语义。例如，他们可以从以前使用的编程语言中引入他们的理解，或者从对特定工具如何解释该符号的理解中引入。我们认为，面向语义的突变测试方法可能有助于发现此类问题。我们试图证明一种面向语义的突变测试方法是可行的，并且可以发现传统语法突变(很可能是其他流行的测试策略)所没有发现的错误。我们认为，这些误解可以表示为语义突变而不是描述，并且为了杀死语义突变而产生的测试数据在发现由这种误解引起的错误时是有效的：它经常会发现现有测试策略产生的测试集(特别是用来杀死传统语法突变的测试集)通常会遗漏的错误。我们还相信，语义突变的产生和测试数据的生成可以自动杀死它们。

Traditional Mutation Testing produces test cases that distinguish between some description N and variants of it. Each variant is produced by applying a mutation operator to N. A test set that is good at distinguishing N from variants of N is likely to be good at finding faults that are similar to applications of the mutation operators. Mutation testing was originally applied to programs but more recently it has been applied to other forms of descriptions such as specifications. Mutants are produced through the application of mutation operators, each of which may be applied to a relevant point in a program in order to produce a mutant. The mutation operators carry out small syntactic changes. For example, + might be replaced by -, > might be replaced by >=, a variable in an expression may be replaced by a constant, or part of an expression may be deleted. The mutation operators are designed to represent syntactically small errors. Typically, mutants are used to either judge the adequacy of a test set (does it distinguish between N and its mutants?) and also to drive test generation (we want a test set that distinguishes between N and its mutants).Traditional mutation testing produces mutants that represent small slips or mistakes in programming and thus represent a class of faults. A mutant program differs from the program under test by a small syntactic change (e.g. a / replaces a * ). However, real developers will also suffer from misunderstandings, especially when moving between description notations. They misapprehend the semantics of the description before them. They may, for example, import their understanding from a previously used programming language, or else from an understanding of how a particular tool interprets the notation. We believe that a semantically oriented mutation testing approach may assist in the discovery of such problems. We seek to show that a semantically oriented mutation testing approach is feasible and can find faults not found by traditional syntactic mutation (and likely, by other popular testing strategies).Misunderstanding the semantics of descriptive notations is a common source of problems in software development. We believe that these misunderstandings can be represented as semantic mutants over descriptions and that test data produced to kill semantic mutants is effective at finding faults caused by such misunderstandings: It will often find faults that are typically missed by test sets produced by extant testing strategies (and in particular, by test sets that are produced to kill traditional syntactic mutants). We also believe that he production of semantic mutants and the generation of test data to kill them can be automated.