Is the Stack Distance Between Test Case and Method Correlated With Test Effectiveness?

Mutation testing is a means to assess the effectiveness of a test suite and its outcome is considered more meaningful than code coverage metrics. However, despite several optimizations, mutation testing requires a significant computational effort and has not been widely adopted in industry. Therefore, we study in this paper whether test effectiveness can be approximated using a more light-weight approach. We hypothesize that a test case is more likely to detect faults in methods that are close to the test case on the call stack than in methods that the test case accesses indirectly through many other methods. Based on this hypothesis, we propose the minimal stack distance between test case and method as a new test measure, which expresses how close any test case comes to a given method, and study its correlation with test effectiveness. We conducted an empirical study with 21 open-source projects, which comprise in total 1.8 million LOC, and show that a correlation exists between stack distance and test effectiveness. The correlation reaches a strength up to 0.58. We further show that a classifier using the minimal stack distance along with additional easily computable measures can predict the mutation testing result of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can be taken into consideration as a light-weight alternative to mutation testing or as a preceding, less costly step to that.Comment: EASE 201

Goal-Oriented Mutation Testing with Focal Methods

Mutation testing is the state-of-the-art technique for assessing the fault-detection capacity of a test suite. Unfortunately, mutation testing consumes enormous computing resources because it runs the whole test suite for each and every injected mutant. In this paper we explore fine-grained traceability links at method level (named focal methods), to reduce the execution time of mutation testing and to verify the quality of the test cases for each individual method, instead of the usually verified overall test suite quality. Validation of our approach on the open source Apache Ant project shows a speed-up of 573.5x for the mutants located in focal methods with a quality score of 80%.Comment: A-TEST 201

Do Null-Type Mutation Operators Help Prevent Null-Type Faults?

The null-type is a major source of faults in Java programs, and its overuse has a severe impact on software maintenance. Unfortunately traditional mutation testing operators do not cover null-type faults by default, hence cannot be used as a preventive measure. We address this problem by designing four new mutation operators which model null-type faults explicitly. We show how these mutation operators are capable of revealing the missing tests, and we demonstrate that these mutation operators are useful in practice. For the latter, we analyze the test suites of 15 open-source projects to describe the trade-offs related to the adoption of these operators to strengthen the test suite

An integrated automatic test data generation system

Abstract The Godzilla automatic test data generator is an integrated collection of tools that implements a relatively new test data generation method, constraint-based testing, that is based on mutation analysis. Constraint-based testing integrates mutation analysis with several other testing techniques, including statement coverage, branch coverage, domain perturbation and symbolic evaluation. Because Godzilla uses a rule-based approach to generate test data, it is easily extendible to allow new testing techniques to be integrated into the current system

A Practical System for Mutation Testing: Help for the Common Programmer

Mutation testing is a technique for unit testing software that, although powerful, is computationally expensive. Recent engineering advances have given us techniques and algorithms for signi cantly reducing the cost of mutation testing. These techniques include a new algorithmic execution technique called schema-based mutation, an approximation technique called weak mutation, a reduction technique called selective mutation, and algorithms for automatic test data generation. This paper outlines a design for a system that will approximate mutation, but in a way that will be accessible to everyday programmers. We envision a system to which a programmer can submit a program unit, and get back a set of input/output pairs that are guaranteed to form an effective test of the unit by being close to mutation adequate

Generating Test Data from . . .

... is to improve our ability to test software that needs to be highly reliable by developing formal techniques for generating test cases from formal speci cational descriptions of the software. Formal specifications give software developers the opportunity to describe exactly what services the software should provide, providing information to software designers in a clear, unambiguous manner. Formal speci cations give test engineers information about the expectations of the software in a form that can be automatically manipulated. This Phase IV, 2000 report presents progress on an empirical evaluation of the e cacy of the transition-pair criterion developed in previous years. Transition-pair tests have been developed for the Flight Guidance System (FGS) and they were run on the faulty versions of FGS developed last year. These tests and the results are summarized in this preliminary report. This report also presents progress in our test data generation tool. This tool has been signi cantly expanded to handle multiple SCR tables, recursively de ned tables, event and condition tables, non-boolean variables, and multiple-variable expressions. It is integrated with the Naval Research Laboratory'