Integrating Software Metrics for Fortran Legacy into an IDE

Software Metrics have being used since the 70s, their purpose is to measure different software attributes, such as complexity and maintainability, to name a few. Software Metrics help programmers obtain valuable information about programs. That information is essential when working with legacy systems. Scientists have been producing Fortran programs for the last six decades, and some of those programs became legacy years ago.We have implemented a set of well known software metrics for Fortran into a widely used IDE (Integrated Development Environment) by means of AST (Abstract Syntax Tree). This integration allows developers to obtain software metrics from their source code while they are programming.XI Workshop de Ingeniería de Softwar

Integrating Software Metrics for Fortran Legacy into an IDE

Software Metrics have being used since the 70s, their purpose is to measure different software attributes, such as complexity and maintainability, to name a few. Software Metrics help programmers obtain valuable information about programs. That information is essential when working with legacy systems. Scientists have been producing Fortran programs for the last six decades, and some of those programs became legacy years ago.We have implemented a set of well known software metrics for Fortran into a widely used IDE (Integrated Development Environment) by means of AST (Abstract Syntax Tree). This integration allows developers to obtain software metrics from their source code while they are programming.XI Workshop de Ingeniería de Softwar

Integrating Software Metrics for Fortran Legacy into an IDE

Software Metrics have being used since the 70s, their purpose is to measure different software attributes, such as complexity and maintainability, to name a few. Software Metrics help programmers obtain valuable information about programs. That information is essential when working with legacy systems. Scientists have been producing Fortran programs for the last six decades, and some of those programs became legacy years ago.We have implemented a set of well known software metrics for Fortran into a widely used IDE (Integrated Development Environment) by means of AST (Abstract Syntax Tree). This integration allows developers to obtain software metrics from their source code while they are programming.XI Workshop de Ingeniería de SoftwareRed de Universidades con Carreras de Informática (RedUNCI

Integrating Software Metrics for Fortran Legacy into an IDE

Software Metrics have being used since the 70s, their purpose is to measure different software attributes, such as complexity and maintainability, to name a few. Software Metrics help programmers obtain valuable information about programs. That information is essential when working with legacy systems. Scientists have been producing Fortran programs for the last six decades, and some of those programs became legacy years ago.We have implemented a set of well known software metrics for Fortran into a widely used IDE (Integrated Development Environment) by means of AST (Abstract Syntax Tree). This integration allows developers to obtain software metrics from their source code while they are programming.XI Workshop de Ingeniería de SoftwareRed de Universidades con Carreras de Informática (RedUNCI

Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims. We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial". We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method. We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk. We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results. Our results show that inverse defect prediction can identify approx. 32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions. Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.Comment: Submitted to PeerJ C

Construction of a Generic Program Representation for Automated Metric Computation

Software code metrics provide a quantitative and qualitative measurement of a software component\u27s ability to perform under a specific set of objectives. Different metrics have been developed for analyzing various aspects of the source code to gain insight into the overall quality of the code under study. There are a variety of open source tools available for computing metrics for applications coded in most of the popular programming languages. However, there is no single tool that computes software metrics for the popular programming languages in use today. To address this problem, we describe an approach to software metric computation that can be applied to the popular programming languages currently in use, including both compiled and interpreted languages. The approach entails leveraging existing parser tools to produce a generalized abstract syntax tree that captures the important syntactic categories required for metric computation. To demonstrate the utility of our approach, we exploit front-end parser tools for the Python and C++ programming languages to produce a generalized abstract syntax tree and then compute software metrics as a form of tree traversal. We describe our results for applying two commonly used metrics to three open source software projects and various code samples written in both Python and C++. The context of this process is then extended to computer programming education, with the specific goal of helping students and programmers improve the quality of their code

An annotated and classified bibliography of software metrics publications : 1988 to 1994

With the growth of the software industry, the measurement of software plays an ever increasing role. In order to provide software metric researchers and practitioners with references so they can quickly identify the references of particular interest to them, over 60 of the many publications on software metrics that have appeared since 1988 are classified into four tables that comprise, respectively, (1) Metrics through the Life Cycle, (2) Classic Metrics, (3) Programming Language Metrics, and (4) New Metrics. Table 1 serves as a complete list of all the classified publications while Table 2, Table 3 and Table 4 are subsets of Table 1. The subset tables present more detailed information than Table 1. The bibliographic reference section contains brief summaries of the publications in the classified tables. As a continuation of the 1988 survey done by V. Cote, P. Bourque, S. Oligny and N. Rivard through the paper, "Software metrics: an overview of recent results", this project was conducted to discover the current trends in software metrics practice, and to report the trend movement from the 1988 paper until now by comparison of the results from the two surveys. All the table comparisons from the two surveys are given in percentages. As a survey, we are fully aware of the limitations of our collection out of the wealth of the publications in the software metrics field, but we are confident that our survey is a good indicator of the practice in the software metrics field. [Résumé abrégé par UMI]

Do internal software quality tools measure validated metrics?

Internal software quality determines the maintainability of the software product and influences the quality in use. There is a plethora of metrics which purport to measure the internal quality of software, and these metrics are offered by static software analysis tools. To date, a number of reports have assessed the validity of these metrics. No data are available, however, on whether metrics offered by the tools are somehow validated in scientific studies. The current study covers this gap by providing data on which tools and how many validated metrics are provided. The results show that a range of metrics that the tools provided do not seem to be validated in the literature and that only a small percentage of metrics are validated in the provided tools