Quantitative Assessment of the Impact of Automatic Static Analysis Issues on Time Efficiency

Background: Automatic Static Analysis (ASA) tools analyze source code and look for code patterns (aka smells) that might cause defective behavior or might degrade other dimensions of software quality, e.g. efficiency. There are many potentially negative code patterns, and ASA tools typically report a huge list of them even in small programs. Moreover, so far, little evidence is available about the negative impact on performance of code patterns identified by such tools. A consequence is that programmers cannot appreciate the benefits of ASA tools and tend not to include them in their workflow. Aims: Quantitatively assess the impact of issues signaled by ASA tools on time efficiency. Method: We select 20 issues and for each of them we set up two source code fragments: one containing the issue and the corresponding refactored version, functionally identical but without the issue. We set up three different platforms, isolated from network and other user programs, then we execute the code fragments, and measure the execution time of both code versions. Results: We find that eleven issues have an actual negative impact on performance. We also compute for each issue an estimation for the delay provoked by a single execution. Conclusions: We produce a set of issues with a verified negative impact on performance. They can be checked easily with an analysis tool and code can be refactored to obtain a provably more efficient code. We also provide the estimated delay cost of each issue in the environments where we conduct the tests. These results can be improved with the help of other researchers: repeating the tests in several platforms would make it possible to build up a wider benchmar

CrocoPat 2.1 Introduction and Reference Manual

CrocoPat is an efficient, powerful and easy-to-use tool for manipulating relations of arbitrary arity, including directed graphs. This manual provides an introduction to and a reference for CrocoPat and its programming language RML. It includes several application examples, in particular from the analysis of structural models of software systems.Comment: 19 pages + cover, 2 eps figures, uses llncs.cls and cs_techrpt_cover.sty, for downloading the source code, binaries, and RML examples, see http://www.software-systemtechnik.de/CrocoPat

A Model-Derivation Framework for Software Analysis

Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581

A Model-Derivation Framework for Software Analysis

Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581

Using Modularity Metrics to assist Move Method Refactoring of Large System

For large software systems, refactoring activities can be a challenging task, since for keeping component complexity under control the overall architecture as well as many details of each component have to be considered. Product metrics are therefore often used to quantify several parameters related to the modularity of a software system. This paper devises an approach for automatically suggesting refactoring opportunities on large software systems. We show that by assessing metrics for all components, move methods refactoring an be suggested in such a way to improve modularity of several components at once, without hindering any other. However, computing metrics for large software systems, comprising thousands of classes or more, can be a time consuming task when performed on a single CPU. For this, we propose a solution that computes metrics by resorting to GPU, hence greatly shortening computation time. Thanks to our approach precise knowledge on several properties of the system can be continuously gathered while the system evolves, hence assisting developers to quickly assess several solutions for reducing modularity issues