Sira Vegas

Reviewing 25 Years of Testing Technique Experiments

Mature knowledge allows engineering disciplines the achievement of predictable results. Unfortunately, the type of knowledge used in software engineering can be considered to be of a relatively low maturity, and developers are guided by intuition, fashion or market-speak rather than by facts or undisputed statements proper to an engineering discipline. Testing techniques determine different criteria for selecting the test cases that will be used as input to the system under examination, which means that an effective and efficient selection of test cases conditions the success of the tests. The knowledge for selecting testing techniques should come from studies that empirically justify the benefits and application conditions of the different techniques. This paper analyzes the maturity level of the knowledge about testing techniques by examining existing empirical studies about these techniques. We have analyzed their results, and obtained a testing technique knowledge classification based on their factuality and objectivity, according to four parameters.

The role of replications in Empirical Software Engineering

Replications play a key role in Empirical Software Engineering by allowing the community to build knowledge about which results or observations hold under which conditions. Therefore, not only can a replication that produces similar results as the original experiment be viewed as successful, but a replication that produce results different from those of the original experiment can also be viewed as successful. In this paper we identify two types of replications: exact replications, in which the procedures of an experiment are followed as closely as possible; and conceptual replications, in which the same research question is evaluated by using a different experimental procedure. The focus of this paper is on exact replications. We further explore them to identify two sub-categories: dependent replications, where researchers attempt to keep all the conditions of the experiment the same or very similar and independent replications, where researchers deliberately vary one or more major aspects of the conditions of the experiment. We then discuss the role played by each type of replication in terms of its goals, benefits, and limitations. Finally, we highlight the importance of producing adequate documentation for an experiment (original or replication) to allow for replication. A properly documented replication provides the details necessary to gain a sufficient understanding of the study being replicated without requiring the replicator to slavishly follow the given procedures.

A Characterisation Schema for Software Testing Techniques

One of the major problems within the software testing area is how to get a suitable set of cases to test a software system. This set should assure maximum effectiveness with the least possible number of test cases. There are now numerous testing techniques available for generating test cases. However, many are never used, and just a few are used over and over again. Testers have little (if any) information about the available techniques, their usefulness and, generally, how suited they are to the project at hand upon, which to base their decision on which testing techniques to use. This paper presents the results of developing and evaluating an artefact (specifically, a characterisation schema) to assist with testing technique selection. When instantiated for a variety of techniques, the schema provides developers with a catalogue containing enough information for them to select the best suited techniques for a given project. This assures that the decisions they make are based on objective knowledge of the techniques rather than perceptions, suppositions and assumptions.

Using differences among replications of software engineering experiments to gain knowledge

In no science or engineering discipline does it make sense to speak of isolated experiments. The results of a single experiment cannot be viewed as representative of the underlying reality. The concept of experiment is closely related to replication. Experiment replication is the repetition of an experiment to double-check its results. Multiple replications of an experiment increase the credibility of its results. Software engineering has tried its hand at the identical repetition of experiments in the way of the natural sciences (physics, chemistry, etc.). After numerous attempts over the years, excepting experiments repeated by the same researchers at the same site, no exact replications have yet been achieved. One key reason for this is the complexity of the software development setting. This complexity prevents the many experimental conditions from being reproduced identically. This paper reports research into whether non-exact replications can be of any use. We propose a process that allows researchers to generate new knowledge when running non-exact replications. To illustrate the advantages of the proposed process, two different replications of an experiment are shown.

The role of non-exact replications in software engineering experiments

In no science or engineering discipline does it make sense to speak of isolated experiments. The results of a single experiment cannot be viewed as representative of the underlying reality. Experiment replication is the repetition of an experiment to double-check its results. Multiple replications of an experiment increase the confidence in its results. Software engineering has tried its hand at the identical (exact) replication of experiments in the way of the natural sciences (physics, chemistry, etc.). After numerous attempts over the years, apart from experiments replicated by the same researchers at the same site, no exact replications have yet been achieved. One key reason for this is the complexity of the software development setting, which prevents the many experimental conditions from being identically reproduced. This paper reports research into whether non-exact replications can be of any use. We propose a process aimed at researchers running non-exact replications. Researchers enacting this process will be able to identify new variables that are possibly having an effect on experiment results. The process consists of four phases: replication definition and planning, replication operation and analysis, replication interpretation, and analysis of the replication’s contribution. To test the effectiveness of the proposed process, we have conducted a multiple-case study, revealing the variables learned from two different replications of an experiment.

Analysis of the influence of communication between researchers on experiment replication

The replication of experiments is a key undertaking in SE. Successful replications enable a disciplines body of knowledge to grow, as the results are added to those of earlier replications. However, replication is extremely difficult in SE, primarily because it is difficult to get a setting that is exactly the same as in the original experiment. Consequently, changes have to be made to the experiment to adapt it to the new site. To be able to replicate an experiment, information also has to be transmitted (usually orally and in writing) between the researchers who ran the experiment earlier and the ones who are going to replicate the experiment. This article examines the influence of the type of communication there is between experimenters on how successful a replication is. We have studied three replications of the same experiment in which different types of communication were used.

Replications types in experimental disciplines

Experiment replication is a key component of the scientific paradigm. The purpose of replication is to verify previously observed findings. Although some Software Engineering (SE) experiments have been replicated, yet, there is still disagreement about how replications should be run in our field. With the aim of gaining a better understanding of how replications are carried out, this paper examines different replication types in other scientific disciplines. We believe that by analysing the replication types proposed in other disciplines it is possible to clarify some of the question marks still hanging over experimental SE replication.

Functional Testing, Structural Testing and Code Reading: What Fault Type Do They Each Detect?

The origin of the study described here is the experiment performed by Basili and Selby, further replicated by Kamsties and Lott, and once again by Wood et al. These experiments investigated the effectiveness and efficiency of different code evaluation techniques (functional and structural testing and code reading). The working hypotheses are the same in all three experiments, although some experimental conditions were changed. The experiments described here use the experiment package elaborated by Kamsties and Lott and examine some of the questions posed as a result of these experiments. Wood et al. concluded in their replication of the original study that the relative effectiveness of the techniques depends on the program and fault type. In fact, they suggest formulating a fault taxonomy based on technique sensitivity. Our study intends to compare the relative effectiveness of the testing techniques and to relate the testing techniques to fault types.

Practical Experiences in the Design and Conduct of Surveys in Empirical Software Engineering

A survey is an empirical research strategy for the collection of information from heterogeneous sources. In this way, survey results often exhibit a high degree of external validity. It is complementary to other empirical research strategies such as controlled experiments, which usually have their strengths in the high internal validity of the findings. While there is a growing number of (quasi-)controlled experiments reported in the software engineering literature, few results of large scale surveys have been reported there. Hence, there is still a lack of knowledge on how to use surveys in a systematic manner for software engineering empirical research.

Understanding replication of experiments in software engineering: A classification

Abstract Context Replication plays an important role in experimental disciplines. There are still many uncertainties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experimenters, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? Objective To improve our understanding of SE experiment replication, in this work we propose a classification which is intend to provide experimenters with guidance about what types of replication they can perform. Method The research approach followed is structured according to the following activities: (1) a literature review of experiment replication in SE and in other disciplines, (2) identification of typical elements that compose an experimental configuration, (3) identification of different replications purposes and (4) development of a classification of experiment replications for SE. Results We propose a classification of replications which provides experimenters in SE with guidance about what changes can they make in a replication and, based on these, what verification purposes such a replication can serve. The proposed classification helped to accommodate opposing views within a broader framework, it is capable of accounting for less similar replications to more similar ones regarding the baseline experiment. Conclusion The aim of replication is to verify results, but different types of replication serve special verification purposes and afford different degrees of change. Each replication type helps to discover particular experimental conditions that might influence the results. The proposed classification can be used to identify changes in a replication and, based on these, understand the level of verification.