… is a consultant for software quality at CQSE GmbH. He studied computer science at the Politehnica University of Bucharest and obtained a PhD in static analysis from the National University of Singapore.
In this post, I give an overview of establishing a code quality control process for a Matlab codebase, like we did for one of our customers. Some of the code examples are taken from three popular open-source applications listed at the Matlab Central webpage: export_fig, T-MATS and CNN-for-Image-Retrieval.
Posted on 03/07/2016 by Dr. Corneliu Popeea
At CQSE, we use Teamscale and static analysis for assessment of technology suitability:
In this post, I show which are the steps to configure Teamscale for such an assessment. This is illustrated using three open-source projects, FindBugs, Google Error Prone and Microsoft StyleCop. First, I use the Teamscale architecture editor and specify for which third-party libraries monitoring dependencies might be desired. Then, the architecture perspective shows the static analysis results and allows quick inspection of dependencies to third-party libraries.
Posted on 08/19/2015 by Dr. Corneliu Popeea
While there is no precise, commonly agreed-on definition of what constitutes a software architecture, it is understood that a software system’s architecture is defined by its decomposition into building blocks and their inter-dependencies. For each pair of components, the architecture defines if and in what way the two components interact which each other. An architecture conformance analysis evaluates how well the implemented architecture matches the specified architecture. Identifying architecture violations using the conformance analysis is a required step for the maintainability of the code base. The release 1.5 of our tool Teamscale adds features that allow the conformance analysis to be better integrated in the development cycle of a project. This article describes the basic concepts needed to understand the editing of architecture and conformance analysis as performed by Teamscale.
Posted on 04/15/2015 by Dr. Corneliu Popeea
Code quality audits aim to assess the quality of a system’s source code and identify weak points in it. Two areas of the quality audits that have been discussed in the previous posts by my colleagues are the redundancy caused by copy/paste and the anomalies that go undetected unless static analysis tools like FindBugs are used periodically to check the source code for defects. In the following, I will outline a small experiment meant to see whether the findings of the static analysis tool FindBugs reside in code blocks that have been copied over in other parts of a system’s source code. To illustrate this experiment, I will use a »Big Data« open-source project, namely Apache Hadoop. It is worth mentioning that, related to its code quality, Apache Hadoop was in the spotlight of the 2014 Report on open-source software quality from our colleagues at Coverity.
Synthesizing software verifiers from proof rules.
Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’12), 2012.
Threader: A Constraint-Based Verifier for Multi-threaded Programs.
Proceedings of the 23rd International Conference on Computer Aided Verification (CAV’11), 2011.
A flow-based approach for variant parametric types.
Proceedings of the 21th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA’06), 2006.