Summary Mutation analysis is a powerful but computationally expensive method to measure the effectiveness of a testing or debugging technique. The high cost is due, in part, to redundant mutants generated by commonly used mutation operators. A mutant is said to beredundantif its outcome can be predicted based on the outcome of other mutants. The execution of those redundant mutants is unnecessary and wastes CPU resources. Moreover, the inclusion of redundant mutants may lead to a skewed mutant detection rate and therefore misrepresent the effectiveness of the assessed testing or debugging technique. This paper extends previous work and makes the following contributions. First, it defines and provides non‐redundant versions of the conditional operator replacement, unary operator insertion, and relational operator replacement mutation operators. Second, it reports on a conducted empirical study using 10 real‐world programmes that comprise a total of 410 000 lines of code. The empirical study used developer‐written and generated test suites. The results show how prevalent redundant mutants are and how their elimination improves the efficiency and accuracy of mutation analysis. In summary, the total mutation analysis run time decreased by more than 20% by removing redundant mutants, and the inclusion of redundant mutants led to an overestimated mutation score for all analysed test suites. Copyright © 2014 John Wiley & Sons, Ltd.

Mutation analysis is an unbiased and powerful method for assessing input values and test oracles. However, in comparison to other techniques, such as those that rely on code coverage, it is a computationally-expensive and time-consuming method, especially for large software systems. This high cost is due, in part, to the fact that many mutation operators generate redundant mutants that may both misrepresent the mutation score and increase the runtime of the mutation analysis process. After showing how the conditional operator replacement(COR) mutation operator can be defined in a redundant-free manner, this paper uses four real-world programs, ranging in size from 3,000 to nearly 40,000 lines of code, to show the prevalence of redundant mutants. Focusing on the conditional operator replacement (COR)and relational operator replacement (ROR) mutation operators that create 41% of all mutants in the chosen programs, the case study reveals that the removal of redundant mutants reduces the runtime of mutation analysis by up to 34%. Additional empirical results show that redundant mutants can lead to a mutation score that is misleadingly overestimated by as much as 10%. Overall, this paper convincingly demonstrates that it is possible to improve the effectiveness and efficiency of a mutation analysis system by identifying and removing redundant mutants.

Mutation analysis is a powerful and unbiased technique to assess the quality of input values and test oracles. However, its application domain is still limited due to the fact that it is a time consuming and computationally expensive method, especially when used with large and complex software systems. Addressing these challenges, this paper makes several contributions to significantly improve the efficiency of mutation analysis. First, it investigates the decrease in generated mutants by applying a reduced, yet sufficient, set of mutants for replacing conditional (COR) and relational (ROR) operators. The analysis of ten real-world applications, with 400,000 lines of code and more than 550,000 generated mutants in total, reveals a reduction in the number of mutants created of up to 37% and more than 25% on average. Yet, since the isolated use of non-redundant mutation operators does not ensure that mutation analysis is efficient and scalable, this paper also presents and experimentally evaluates an optimized workflow that exploits the redundancies and runtime differences of test cases to reorder and split the corresponding test suite. Using the same ten open-source applications, an empirical study convincingly demonstrates that the combination of non-redundant operators and prioritization leveraging information about the runtime and mutation coverage of tests reduces the total cost of mutation analysis further by as much as 65%.

Mutation analysis is an effective, yet often time-consuming and difficult-to-use method for the evaluation of testing strategies. In response to these and other challenges, this paper presents MAJOR, a fault seeding and mutation analysis tool that is integrated into the Java Standard Edition compiler as a non-invasive enhancement for use in any Java-based development environment. MAJOR reduces the mutant generation time and enables efficient mutation analysis. It has already been successfully applied to large applications with up to 373,000 lines of code and 406,000 mutants. Moreover, MAJOR's domain specific language for specifying and adapting mutation operators also makes it extensible. Due to its ease-of-use, efficiency, and extensibility, MAJOR is an ideal platform for the study and application of mutation analysis.

Nowadays, Rich Internet Applications (RIAs) gain more and more popularity. They allow to deliver internet applications that offer a user expericence known from desktop applications. Since in traditional browser centric internetapplications the server delivers the content that is to be displayed, the applications can flexibly adapt their functionality to the application’s state and context (e.g. the permissions granted to the current user) at any time. As for RIAs, things are different. Normally, they exist in a pre-compiled form—for example as a swf-file. Therefore, they don’t allow any dynamic customization of their functionality during runtime. However, it would be eligible that components used in a RIA (e.g. navigation structures) are able to adapt automatically—whenever any change in the application’s state or context occurs—to that new state or context. Thus, the RIA components could provide a view onto the application that is totally in sync with its current status and context. In this paper, we present a pattern which allows an easy implementation of this behavior. Then we will show a case study where this pattern has been applied successfully.

With the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today's automotive industry. Field data analysis supports corrective actions in development, production and after sales support. We decompose the requirements and show that association rules, being a popular approach to generating ex- planative models, still exhibit shortcomings. Recently proposed interactive rule cubes are a promising alternative. We extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. Moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. All this is currently in use as an interactive tool for warranty data analysis in the automotive industry. A real-world case study shows how engineers successfully use it in identifying root causes of quality issues.

Gleason grading is a common method used by pathologists to determine the aggressivity of prostate cancer on the basis of histological slide preparations. The advantage of this grading system is a good correlation with the biological behavior of the tumor, while its drawback is the subjectivity underlying the judgements of pathologists. Therefore, an automation of Gleason grading would be desirable. In this paper, we examined 780 digitized grayscale images of 78 different cases, which were split into a training and a test set. We developed two methods based on combinations of morphological characteristics like area fraction, line length, and Euler number to classify into the categories "Gleason score < 7" and "Gleason score > or = 7." In particular, the distinction between these two classes has great impact on the prognosis of patients. The agreement of each method with visual diagnosis was 87.18% and 92.31% within the training set and 66.67% and 64.10% within the test set, respectively.