week 08 - Test cases as a measurement instrument, Agile Assessment Methods: Current State of the Art and Software Testing in the DevOps Context
Method: Two industry experiments have been measured using two different test suites, one generated using an ad-hoc method and another using equivalence partitioning. The accuracy of the measures has been studied using standard procedures, such as ISO 5725, Bland-Altman and Interclass Correlation Coefficients.
Results: There are differences in the values of the response variables up to +-60%, depending on the test suite (ad-hoc vs. equivalence partitioning) used.
Conclusions: The disclosure of datasets and analysis code is insufficient to ensure the reproducibility of SE experiments. Experimenters should disclose all experimental materials needed to perform independent measurements and re-analysis.
Agility Assessment (AA) comprises tools, assessment techniques, and frameworks that focus on indicating how a company or a team is applying agile techniques and eventually pointing out problems in adopting agile practices at a project-level, organization-level or individual-level. There are many approaches for AA such as agility assessment models, agility checklists, agility surveys, and agility assessment tools. This report presents the state of the art approaches that support agility assessment.
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DevOps is a philosophy and framework that allows software development and operations teams to work in a coordinated manner, with the purpose of developing and releasing software quickly and cheaply. However, the effectiveness and benefits of DevOps depend on several factors, as reported in the literature. In particular, several studies have been published on software test automation, which is a cornerstone for the continuous integration phase in DevOps, which needs to be identified and classified. This study consolidates and classifies the existing literature on automated tests in the DevOps context. For the study, a systematic mapping study was performed to identify and classify papers on automated testing in DevOps based on 8 research questions. In the query of 6 relevant databases, 3,312 were obtained; and then, after the selection process, 299 papers were selected as primary studies. Researchers maintain a continuing and growing interest in software testing in the DevOps context. Most of the research (71.2%) is carried out in the industry and is done on web applications and SOA. The most reported types of tests are unit and integration tests.
A software product is expected to be subjected to critical evaluation on its quality attributes in order to ascertain that target quality requirements are met, and that those quality attributes responsible for revealing software quality are not omitted in the software development process. Software metrics are essential to accomplish the task. This paper has carried out an exploratory study of software measurement and software metrics in tandem. The study took into cognizance the interwoven nature of the duo in measuring and revealing software quality. The study formulated a model that expressed the mutual bonding that propels both measurement and metrics to describing software quality in numeric quantities of software attributes. The study identified six software attributes whose values are considered enough quantities to reveal the quality of a software product. The identification enabled the study to create a model equation aimed at giving a numeric value for the complete evaluation of a software system. The result of the implementation of the six software attributes into the model equation showed that two software products employed in the study are of high-quality, having quality values of 0.93 and 0.86 respectively. The attributes produced values that confirmed the maintainability (25 seconds & 20 seconds respectively) and reliability (0.78 & 0.80 respectively) of both software products among other differing features that characterize them.
Selecting a research method for empirical software engineering research is problematic because the benefits and challenges to using each method are not yet well catalogued. Therefore, this chapter describes a number of empirical methods available. It examines the goals of each and analyzes the types of questions each best addresses. Theoretical stances behind the methods, practical considerations in the application of the methods and data collection are also briefly reviewed. Taken together, this information provides a suitable basis for both understanding and selecting from the variety of methods applicable to empirical software engineering.
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