A survey on software testability

Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective: Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method: To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers. Results: The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability. Conclusion: This paper serves for both researchers and practitioners as an "index" to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects

Orientation de l'effort des tests unitaires dans les systèmes orientés objet : une approche basée sur les métriques logicielles

Les logiciels actuels sont de grandes tailles, complexes et critiques. Le besoin de qualité exige beaucoup de tests, ce qui consomme de grandes quantités de ressources durant le développement et la maintenance de ces systèmes. Différentes techniques permettent de réduire les coûts liés aux activités de test. Notre travail s’inscrit dans ce cadre, est a pour objectif d’orienter l’effort de test vers les composants logiciels les plus à risque à l’aide de certains attributs du code source. À travers plusieurs démarches empiriques menées sur de grands logiciels open source, développés avec la technologie orientée objet, nous avons identifié et étudié les métriques qui caractérisent l’effort de test unitaire sous certains angles. Nous avons aussi étudié les liens entre cet effort de test et les métriques des classes logicielles en incluant les indicateurs de qualité. Les indicateurs de qualité sont une métrique synthétique, que nous avons introduite dans nos travaux antérieurs, qui capture le flux de contrôle ainsi que différentes caractéristiques du logiciel. Nous avons exploré plusieurs techniques permettant d’orienter l’effort de test vers des composants à risque à partir de ces attributs de code source, en utilisant des algorithmes d’apprentissage automatique. En regroupant les métriques logicielles en familles, nous avons proposé une approche basée sur l’analyse du risque des classes logicielles. Les résultats que nous avons obtenus montrent les liens entre l’effort de test unitaire et les attributs de code source incluant les indicateurs de qualité, et suggèrent la possibilité d’orienter l’effort de test à l’aide des métriques.Current software systems are large, complex and critical. The need for quality requires a lot of tests that consume a large amount of resources during the development and the maintenance of systems. Different techniques are used to reduce the costs of testing activities. Our work is in this context. It aims to guide the unit testing effort distribution on the riskiest software components using the source code attributes. We conducted several empirical analyses on different large object-oriented open source software systems. We identified and studied several metrics that characterize the unit testing effort according to different perspectives. We also studied their relationships with the software class metrics including quality indicators. The quality indicators are a synthetic metric that we introduced in our previous work. It captures control flow and different software attributes. We explored different approaches for unit testing effort orientation using source code attributes and machine learning algorithms. By grouping software metrics, we proposed an effort orientation approach based on software class risk analysis. In addition to the significant relationships between testing metrics and source code attributes, the results we obtained suggest the possibility of using source code metrics for unit testing effort orientation