The role of software measures and metrics in studies of program comprehension

Mathias, Karl S.; Cross, James H.; Hendrix, T. Dean; Barowski, Larry A.

doi:10.1145/306363.306381

Cited by 11 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are numerous software clustering techniques that can be used during reverse engineering to identify software components (Koschke, 2000). Frank Tip provides a survey of program slicing techniques (Tip, 1995) and Mathias et al discuss the role of various software metrics on program comprehension (Mathias, 1999).…”

Section: Tool Researchmentioning

confidence: 99%

Theories, tools and research methods in program comprehension: past, present and future

Storey

2006

Software Qual J

View full text Add to dashboard Cite

Program comprehension research can be characterized by both the theories that provide rich explanations about how programmers understand software, as well as the tools that are used to assist in comprehension tasks. In this paper, I review some of the key cognitive theories of program comprehension that have emerged over the past thirty years. Using these theories as a canvas, I then explore how tools that are commonly used today have evolved to support program comprehension. Specifically, I discuss how the theories and tools are related and reflect on the research methods that were used to construct the theories and evaluate the tools. The reviewed theories and tools are distinguished according to human characteristics, program characteristics, and the context for the various comprehension tasks. Finally, I predict how these characteristics will change in the future and speculate on how a number of important research directions could lead to improvements in program comprehension tool development and research methods.

show abstract

Section: Tool Researchmentioning

confidence: 99%

Theories, tools and research methods in program comprehension: past, present and future

Storey

2006

Software Qual J

View full text Add to dashboard Cite

show abstract

“…According to Finkelstein and Leaning [2], software measurement is the process of defining numbers or symbols for the attributes of software entities (e.g., the attributes size, maintainability, and efficiency), which is a challenging endeavor given the abstract nature of software [3]. Mathias et al [4] claimed that whenever a value is defined for an attribute, there is a measure, and a measure is a combination of measures. Even though, in practice, the terms "measure" and "measure" are used interchangeably; in this article, we use the term "measure" as defined in ISO/IEC 15939:2007 [5].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Networks-Based Method to Analyze the Validity of the Data of Software Measurement Programs

et al. 2020

View full text Add to dashboard Cite

Measures are essential resources to improve quality and control costs during software development. One of the main factors for having successful software measurement programs is measure trustworthiness, defined as how much a user can trust a measure to use it with confidence. Such confidence enables the users to interpret them and use them for supporting decision-making. ISO/IEC 15939:2007 describes four stages that influence such interpretability: measure selection, measure validation, threshold definition, and data validation. Regarding data validation, which directly impacts the measure's trustworthiness value, the literature is scarce in supporting it. This article aims to detail a method that uses Bayesian networks for supporting data validation and shows its application in practice to four software development projects from one company. The proposed method uses Bayesian networks to calculate the degree in which a collected number or symbol represents the real value for the measures, and is integrated with GQM for assessing the measurement program's goals. First, the measurement users must create GQM model hierarchical structures, use it as input for constructing the Bayesian network, validate the Bayesian network, and, finally, use it to support decision-making. A tool to support the proposed method was developed and is freely available. Further, herein, the results of the case study are presented. We identified four benefits in using the proposed method: Externalization, Diagnosis support, Measure interpretation improvement, and Decision-making support. Given this, even though the initial effort to use the proposed method lasted, on average, one hour and fourteen minutes, the benefits of using it outweighed the effort of applying it. Therefore, our findings suggest that there was a positive intention in adopting the proposed method in practice.INDEX TERMS Goal-Question-Metric; Bayesian network; Software measurement.

show abstract

“…According to Mathias et al [13], a * renata.saraiva@embedded.ufcg.edu.br † mirko.perkusich@embedded.ufcg.edu.br ‡ hyggo@embedded.ufcg.edu.br § perkusic@embedded.ufcg.edu.br measurement occurs when an attribute is measured, that is, when a value is assigned to it. By combining this measure with useful information, we have a metric (e.g., average number of defects per module).…”

Section: Introductionmentioning

confidence: 99%

“…By combining this measure with useful information, we have a metric (e.g., average number of defects per module). In practice, the terms "metric" and "measure" are often used interchangeably [13]. In this paper, we use the definitions presented by Mathias et al [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Process to Calculate the Uncertainty of Software Metrics-based Models Using Bayesian Networks

Saraiva¹,

Perkusich²,

Almeida³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Software metrics are essential resources in software enterprises. They can be used to support decision-making and, consequently, reduce costs, improve the productivity of the team and the quality of products delivered. On the other hand, this is only possible if the metrics are valid. Although there are studies related to software metrics validity, none present a solution to represent the uncertainties of the metrics selected to measure the attributes of the entities. In this paper, we present a process to build Bayesian networks to represent the uncertainties of software metrics-based models. The proposed solution is composed of two activities and focuses on the selection and validation of metrics to construct the Bayesian networks. We validated the model with simulated scenarios. Given the successful results, we concluded that the proposed solution is promising. This paper complements the state of the art by showing how to complement a popular metric selection technique, GQM, with information to model uncertainties of the metrics using the concepts of metric validation and Bayesian networks.

show abstract

The role of software measures and metrics in studies of program comprehension

Cited by 11 publications

References 14 publications

Theories, tools and research methods in program comprehension: past, present and future

Theories, tools and research methods in program comprehension: past, present and future

A Bayesian Networks-Based Method to Analyze the Validity of the Data of Software Measurement Programs

A Process to Calculate the Uncertainty of Software Metrics-based Models Using Bayesian Networks

Contact Info

Product

Resources

About