scholarly journals Static Source Code Analysis in Pattern Recognition, Performance Optimization and Software Maintainability

2018 ◽  
Author(s):  
Dénes Bán
Keyword(s):  
Pattern Recognition ◽  
Performance Optimization ◽  
Recognition Performance ◽  
Source Code ◽  
Source Code Analysis ◽  
Code Analysis ◽  
Static Source ◽  
Software Maintainability

scholarly journals Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Gábor Antal ◽  
Zoltán Tóth ◽  
Péter Hegedűs ◽  
Rudolf Ferenc
Keyword(s):  
Software Maintenance ◽  
Positive Impact ◽  
Source Code ◽  
Code Analysis ◽  
Static Source ◽  
Static Code Analysis ◽  
Function Calls ◽  
Hybrid Code ◽  
Code Metrics ◽  
Scripting Language

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

scholarly journals Securing Web Applications through a Framework of Source Code Analysis

2019 ◽  
Vol 15 (12) ◽  
pp. 1780-1794
Author(s):  
Alka Agrawal ◽  
Mamdouh Alenezi ◽  
Rajeev Kumar ◽  
Raees Ahmad Khan
Keyword(s):  
Web Applications ◽  
Source Code ◽  
Source Code Analysis ◽  
Code Analysis

An Automated Approach for Constructing Framework Instantiation Documentation

2020 ◽  
Vol 30 (04) ◽  
pp. 575-601
Author(s):  
Raquel Fialho de Queiroz Lafetá ◽  
Thiago Fialho de Queiroz Lafetá ◽  
Marcelo de Almeida Maia
Keyword(s):  
Empirical Study ◽  
Human Subjects ◽  
Source Code ◽  
High Quality ◽  
Source Code Analysis ◽  
Code Analysis ◽  
Empirical Assessment ◽  
Significant Difference ◽  
Api Documentation ◽  
Substantial Effort

A substantial effort, in general, is required for understanding APIs of application frameworks. High-quality API documentation may alleviate the effort, but the production of such documentation still poses a major challenge for modern frameworks. To facilitate the production of framework instantiation documentation, we hypothesize that the framework code itself and the code of existing instantiations provide useful information. However, given the size and complexity of existent code, automated approaches are required to assist the documentation production. Our goal is to assess an automated approach for constructing relevant documentation for framework instantiation based on source code analysis of the framework itself and of existing instantiations. The criterion for defining whether documentation is relevant would be to compare the documentation with an traditional framework documentation, considering the time spent and correctness during instantiation activities, information usefulness, complexity of the activity, navigation, satisfaction, information localization and clarity. We propose an automated approach for constructing relevant documentation for framework instantiation based on source code analysis of the framework itself and of existing instantiations. The proposed approach generates documentation in a cookbook style, where the recipes are programming activities using the necessary API elements driven by the framework features. We performed an empirical study, consisting of three experiments with 44 human subjects executing real framework instantiations aimed at comparing the use of the proposed cookbooks to traditional manual framework documentation (baseline). Our empirical assessment shows that the generated cookbooks performed better or, at least, with non-significant difference when compared to the traditional documentation, evidencing the effectiveness of the approach.

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