When interacting with their software systems, users may have to deal with problems like crashes, failures, and program instability. Faulty software running in the field is not only the consequence of ineffective in-house verification and validation techniques, but it is also due to the complexity and diversity of the interactions between an application and its environment. Many of these interactions can be hardly predicted at testing time, and even when they could be predicted, often there are so many cases to be tested that they cannot be all feasibly addressed before the software is released. This Ph.D. thesis investigates the idea of addressing the faults that cannot be effectively addressed in house directly in the field, exploiting the field itself as testbed for running the test cases. An enormous number of diverse environments would then be available for testing, giving the possibility to run many test cases in many different situations, timely revealing the many failures that would be hard to detect otherwise.
Finally, there are analysis techniques . For instance, residual test coverage monitoring @cite_2 collects coverage data from the field to complement the results of structural testing obtained in-house. Recently, Ohmann et Al. @cite_18 proposed a novel approach to limit the amount of instrumentation necessary to collect coverage data, increasing the applicability of residual test coverage in the field.
@cite_18 @cite_2
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Structural coverage criteria are often used as an indicator of the thoroughness of testing, but complete satisfaction of a criterion is seldom achieved. When a software product is released with less than 100 coverage, testers are explicitly or implicitly assuming that executions satisfying the remaining test obligations (the residue) are either infeasible or occur so rarely that they have negligible impact on quality. Violation of this assumption indicates shortcomings in the testing process. Monitoring in the deployed environment, even in the beta test phase, is typically limited to error and sanity checks. Monitoring the residue of test coverage in actual use can provide additional useful information, but it is unlikely to be accepted by users unless its performance impact is very small. Experience with a prototype tool for residual test coverage monitoring of Java programs suggests that, at least for statement coverage, the simple strategy of removing all probes except those corresponding to the residue of coverage testing reduces execution overhead to acceptably low levels. (Background) Empirical Software Engineering (SE) strives to provide empirical evidence about the pros and cons of SE approaches. This kind of knowledge becomes relevant when the issue is whether to change from a currently employed approach to a new one or not. An informed decision is required and is particularly important in the development of safety-critical systems. For example, for the safety analysis of safety-critical embedded systems, methods such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) are used. With the advent of model-based systems and software development, the question arises whether safety engineering methods should also be adopted. New technologies such as Component Integrated Fault Trees (CFT) come into play. Industry demands to know the benefits of these new methods over established ones such as Fault Trees (FT). (Methods) For the purpose of comparing CFT and FT with regard to the capabilities of the safety analysis methods (such as quality of the results) and to the participants' rating of the consistency, clarity, and maintainability of the methods, we designed a comparative study as a controlled experiment using a within-subject design. The experiment was run with seven academic staff members working towards their PhD. The study was replicated with eleven domain experts from industry. (Results) Although the analysis of the tasks' solutions showed that the use of CFT did not yield a significantly different number of correct or incorrect solutions, the participants rated the modeling capacities of CFT higher in terms of model consistency, clarity, and maintainability. (Conclusion) From this first evidence, we conclude that CFT have the potential of being beneficial for companies looking for a safety analysis approachfor projects using model-based development. Database applications features such as Structured Query Language programming, exception handling, integrity constraints, and table triggers pose difficulties for maintenance activities, especially for regression testing that follows modifying database applications. In this chapter, we address these difficulties and propose a two-phase regression testing methodology. In phase 1, we explore control flow and data flow analysis issues of database applications. Then, we propose an impact analysis technique that is based on dependencies that exist among the components of database applications. This analysis leads to selecting test cases from the initial test suite for regression testing the modified application. In phase 2, we propose two algorithms for reducing the number of regression test cases. The Graph Walk algorithm walks through the control flow graph of database modules and selects a safe set of test cases to retest. The Call Graph Firewall algorithm uses a firewall for the inter-procedural level. Our experience with this regression testing methodology shows that the impact analysis technique is adequate for selecting regression tests and that phase 2 techniques can be used for further reduction in the number of these tests. Test coverage is a promising measure of test effectiveness and development organizations are interested in cost-effective levels of coverage that provide sufficient fault removal with contained testing effort. We have conducted a multiple-case study on two dissimilar industrial software projects to investigate if test coverage reflects test effectiveness and to find the relationship between test effort and the level of test coverage. We find that in both projects the increase in test coverage is associated with decrease in field reported problems when adjusted for the number of prerelease changes. A qualitative investigation revealed several potential explanations, including code complexity, developer experience, the type of functionality, and remote development teams. All these factors were related to the level of coverage and quality, with coverage having an effect even after these adjustments. We also find that the test effort increases exponentially with test coverage, but the reduction in field problems increases linearly with test coverage. This suggests that for most projects the optimal levels of coverage are likely to be well short of 100 .