A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity

Early prediction of quality of service using interface-level metrics, code-level metrics, and antipatterns

Context: A large number of source code metrics are reported in the literature. It is necessary to systematically collect, describe and classify source code metrics to support research and practice. Objective: We aim to utilize existing secondary studies to develop a catalog of source code metrics together with their descriptions. The catalog will also provide information about which units of code (e.g., operators, operands, lines of code, variables, parameters, code blocks, or functions) are used to measure the internal quality attributes and the scope on which they are collected. Method: We conducted a tertiary study to identify secondary studies reporting source code metrics. We have classified the source code metrics according to the measured internal quality attributes, the units of code used in the measures, and the scope at which the source code metrics are collected. Results: From 711 secondary studies, we identified 52 relevant secondary studies. We reported 423 source code metrics together with their descriptions and the internal quality attributes they measure. Source code metrics predominantly incorporate function as a unit of code to measure internal quality attributes. In contrast, several source code metrics use more than one unit of code when measuring internal quality attributes. Nearly 51% of the source code metrics are collected at the class scope, while almost 12% and 15% of source code metrics are collected at module and application levels, respectively. Conclusions: Researchers and practitioners can use the extensive catalog to assess which source code metrics meet their individual needs based on the description and classification scheme presented.

Mobile applications have been around for a long time and proved to be a new excited market where everyone want to engage themselves. They have become more important than Web pages nowadays. Companies are giving more preference to mobile apps as compared to Web sites because of their user friendliness , better visibility and ease of social networking. This paper compares static code metrics and process metrics for predicting defects in an open source mobile applications. Correlation coefficient, mean absolute error and root mean squared error with process metrics as predictors are significantly better than with code metrics as predictors. Also the combined model based on process and code metrics is better than the model based on code metrics. It is shown that process metrics based defect prediction models are better for mobile applications in all 7 machine learning techniques used for modelling.

Block-based programming has been overwhelmingly successful in revitalizing introductory computing education and in facilitating end-user development. However, poor code quality makes block-based programs hard to understand, modify, and reuse, thus hurting the educational and productivity effectiveness of blocks. There is great potential benefit in empowering programmers in this domain to systematically improve the code quality of their projects. Refactoring--improving code quality while preserving its semantics--has been widely adopted in traditional software development. In this work, we introduce refactoring to Scratch. We define four new Scratch refactorings: Extract Custom Block, Extract Parent Sprite, Extract Constant, and Reduce Variable Scope. To automate the application of these refactorings, we enhance the Scratch programming environment with powerful program analysis and transformation routines. To evaluate the utility of these refactorings, we apply them to remove the code smells detected in a representative dataset of 448 Scratch projects. We also conduct a between-subjects user study with 24 participants to assess how our refactoring tools impact programmers. Our results show that refactoring improves the subjects' code quality metrics, while our refactoring tools help motivate programmers to improve code quality.

The correct prediction of faulty modules or classes has a number of advantages such as improving the quality of software and assigning capable development resources to fix such faults. There have been different kinds of fault/defect prediction models proposed in literature, but a great majority of them makes use of static code metrics as independent variables for making predictions. Recently, process metrics have gained a considerable attention as alternative metrics to use for making trust-worthy predictions. The objective of this paper is to investigate different combinations of static code and process metrics for evaluating fault prediction performance. We have used publicly available data sets, along with a frequently used classifier, Naive Bayes, to run our experiments. We have, both statistically and visually, analyzed our experimental results. The statistical analysis showed evidence against any significant difference in fault prediction performances for a variety of different combinations of metrics. This reinforced earlier research results that process metrics are as good as predictors of fault proneness as static code metrics. Furthermore, the visual inspection of box plots revealed that the best set of metrics for fault prediction is a mix of both static code and process metrics. We also presented evidence in support of some process metrics being more discriminating than others and thus making them as good predictors to use.

A significant number of Free Software projects has been widely used and considered successful. However, there is an even larger number of them that cannot overcome the initial step towards building an active community of users and developers. In this study, we investigated whether there are relationships between source code metrics and attractiveness, i.e., the ability of a project to attract users and developers. To verify these relationships, we analyzed 6,773 Free Software projects from the SourceForge.net repository. The results indicated that attractiveness is indeed correlated to some source code metrics. This suggests that measurable attributes of the project source code somehow affect the decision to contribute and adopt a Free Software project. The findings described in this study show that it is relevant for project leaders to monitor source code quality, most specifically a few objective metrics, since these can have a positive influence in their chances of forming a community of contributors and users around the software, enabling further enhancement in its quality.

Bug prediction is an approach that helps make bug detection more automated during software development. Based on a bug dataset a prediction model is built to locate future bugs. Bug datasets contain information about previous defects in the code, process metrics, or source code metrics, etc. As code smells can indicate potential flaws in the source code, they can be used for bug prediction as well.

Prediction of faults in a proposed software is helpful in deciding the amount of effort to be given for software development. We observed that, a good number of authors hypothesized that the performance of fault prediction model depends on the source code metrics which are used as input of the model. Feature selection technique is a process of selecting suitable set of source code metrics which may improve the performance of fault prediction model. In this work, genetic algorithm (GA) has been applied as feature selection technique to select the suitable set of source code metrics. This selected set of source code metrics are used as requisite input data to develop a classifier using five different classification techniques such as logistic regression, extreme learning machine, support vector machine (SVM) with three different kernel functions (linear, polynomial, and radial basis kernel functions) in order to predict the faulty and non-faulty classes. In this study, we propose a cost evaluation framework to perform cost based analysis for evaluating the effectiveness of fault prediction model. We perform experiments on thirty number of Java Open Source projects. From the obtained results, it is observed that the model developed using selected set of source code metrics obtained better result as compared to all metrics. From costs analysis framework, it is observed that the developed fault prediction model is best suitable for software with % of faulty classes less than the threshold value depending on fault identification efficiency (low-46.44%, median-45.37%, and high-36.63%).

Fault localization aims to support the debugging activities of human developers by highlighting the program elements that are suspected to be responsible for the observed failure. Spectrum Based Fault Localization (SBFL), an existing localization technique that only relies on the coverage and pass/fail results of executed test cases, has been widely studied but also criticized for the lack of precision and limited effort reduction. To overcome restrictions of techniques based purely on coverage, we extend SBFL with code and change metrics that have been studied in the context of defect prediction, such as size, age and code churn. Using suspiciousness values from existing SBFL formulas and these source code metrics as features, we apply two learn-to-rank techniques, Genetic Programming (GP) and linear rank Support Vector Machines (SVMs). We evaluate our approach with a ten-fold cross validation of method level fault localization, using 210 real world faults from the Defects4J repository. GP with additional source code metrics ranks the faulty method at the top for 106 faults, and within the top five for 173 faults. This is a significant improvement over the state-of-the-art SBFL formulas, the best of which can rank 49 and 127 faults at the top and within the top five, respectively.

Technical debt is a metaphor that refers to all the consequences of poorly written code and trade-offs in development. Early technical debt diagnosis is important for software developers because it allows planning for software maintenance and improvement activities, such as refactoring, to prevent system degradation. Several studies have been conducted in the literature on the identification of the technical debt and its consequences, thanks to useful tools for identifying the problem within the source code. On the other hand, this work aims to explore a deep learning approach to predict the rise of technical debt in software code by leveraging the knowledge of changing quality metrics. For validation of the approach, a large dataset was built, related to four known Java software projects, with the collection of numerous class-level code quality metrics. The results obtained show the effectiveness of the proposed approach in predicting the development of Technical Debt within the source code. We obtained an F1 score of 0.99 for two of the chosen software systems and greater than 0.91 for the remaining two.

Software defect prediction is an important part to ensure software quality and increase cost-efficiency. The core aspect prior to software defect prediction is software metrics. More research of process metrics existed and rapidly developed as the fact that process metrics can outperform code metrics. The development of process metrics is diverse aligning with various process metrics generated in the different publication using different dataset and granularity level, thus the development state of process metrics need to be further tracked. Recent research on software defect prediction using process metrics has been carried which complements or argues another studied related to the implementation of process metrics, dataset, and granularity level. This literature review has collected 17 literature studies that aim to depict the current research used of software defect prediction applying software process metrics, dataset, and granularity. Seven commonly used process metrics are identified such as NDC, NML, ADEV, EXP, LA, LD, and COMM. Process metrics development has been implemented in the previous research such as the application of aggregated process metrics and commits detailed information metrics. The selected research shows that 87% of research are using a public dataset while the rest are using a combined dataset. This result depicts the limited accessibility of private and industrial dataset for defect prediction. Following analysis of software granularity presents 4 levels of granularity used in selected research such as a functional, class, file, and features level. File-level defect predictions are mostly used for defect prediction in selected research and prove good defect prediction performance. The results of this literature review also identify the proposed studies which is software defect prediction using developed process metrics in the different datasets and features-level granularity to strengthen the previous research.

Keywords: percutaneous coronary intervention; structural heart disease intervention; congential heart disease; adults; congenital heart disease; pediatrics

The Code Quality Risk Assessment (CQRA) is a novel process within NASA to quantify structural code quality of mission software. By balancing source code metrics, static-code analysis tool output, statistical random sampling, and experienced engineering judgment, a robust measure of code quality is produced. This measure is complemented by ample detail on which aspects of the source-code are positive and which aspects could use improvement. CQRA as a process can be an integral part of improving source quality from the early stages of development, which has been shown to decrease the overall need for code maintenance and the cost of cleaning up issues that will inevitably show up later in the development lifecycle. CQRA as a technique has identified key risks, weaknesses, and strengths with respect to code quality on numerous NASA missions and will continue to be a resource applied to improve mission-critical code quality.

This paper presents an approach for the application of clustering algorithms to uncover computer programming learning profiles by using evidence extracted from source code metrics. A system for automatic assessment of programming activities featuring capture of source code metrics was developed, in order to build a dataset containing metrics extracted from programs developed by beginners in a computing course. The dataset was submitted to three clustering algorithms. The results were promising when clustering students according to these indicators using the K-means algorithm.

Due to the constant evolution of technology, each day brings new programming languages, development paradigms, and ways of evaluating processes. This is no different with source code metrics, where there is always new metric classes. To use a software metric to support decisions, it is necessary to understand how to perform the metric collection, calculation, interpretation, and analysis. The tasks of collecting and calculating source code metrics are most often automated, but how should we monitor them during the software development cycle? Our research aims to assist the software engineer to monitor metrics of vulnerability threats present in the source code through a reference prediction model, considering that real world software have non-functional security requirements, which implies the need to know how to monitor these requirements during the software development cycle. As a first result, this paper presents an empirical study on the evolution of the Linux project. Based on static analysis data, we propose low complexity models to study flaws in the Linux source code. About 391 versions of the project were analyzed by mining the official Linux repository using an approach that can be reproduced to perform similar studies. Our results show that it is possible to predict the number of warnings triggered by a static analyzer for a given software project revision as long as the software is continuously monitored.