Controversy corner

Understanding someone else's code: Analysis of experiences

Unit tests are an important artifact that supports the software development process in several ways. For example, when a test fails, its name can provide the first step towards understanding the purpose of the test. Unfortunately, unit tests often lack descriptive names. In this paper, we propose a new, pattern-based approach that can help developers improve the quality of test names of JUnit tests by making them more descriptive. It does this by detecting non-descriptive test names and in some cases, providing additional information about how the name can be improved. Our approach was assessed using an empirical evaluation on 34352 JUnit tests. The results of the evaluation show that the approach is feasible, accurate, and useful at discriminating descriptive and non-descriptive names with a 95% true-positive rate.

Process models are key in facilitating communication in organizations and in designing process-aware information systems. Organizations are facing increasingly larger and more complex processes, which pose difficulties to the understandability of process models. The literature reports several factors that are considered to influence the understandability of process models. However, these studies typically focus on testing of a limited set of factors. A work that collects, abstracts and synthesizes an in-depth summary of the current literature will help in developing the research in this field.

We conducted a systematic literature review (SLR) focusing on the empirical studies in the existing literature in order to better understand the state of the research on process model understandability, and identify the gaps and opportunities for future research.

We searched the studies between the years 1995 and 2015 in established electronic libraries. Out of 1066 publications retrieved initially, we selected 45 publications for thorough analysis. We identified, analyzed and categorized factors that are considered to influence the understandability of process models as studied in the literature using empirical methods. We also analyzed the indicators that are used to quantify process model understandability.

Our analysis identifies several gaps in the field, as well as issues of inconsistent findings regarding the effect of some factors, unbalanced emphasis on certain indicators, and methodological concerns.

The existing research calls for comprehensive empirical studies to contribute to a better understanding of the factors of process model understandability. Our study is a comprehensive source for researchers working on the understandability of process models and related fields, and a useful guide for practitioners aiming to generate understandable process models.

In order to make good decisions about the design of information systems, an essential skill is to understand process models of the business domain the system is intended to support. Yet, little knowledge to date has been established about the factors that affect how model users comprehend the content of process models. In this study, we use theories of semiotics and cognitive load to theorize how model and personal factors influence how model viewers comprehend the syntactical information of process models. We then report on a four-part series of experiments, in which we examined these factors. Our results show that additional semantical information impedes syntax comprehension, and that theoretical knowledge eases syntax comprehension. Modeling experience further contributes positively to comprehension efficiency, measured as the ratio of correct answers to the time taken to provide answers. We discuss implications for practice and research.