Combining Static Analysis and Test Generation for {C} Program Debugging

International audienceSoftware validation remains crucial in software development process. Traditionally viewed as separate domains, static and dynamic analysis have complementary strengths and weaknesses and can be both applied to program validation and verification. This paper presents our ongoing work on a tool prototype called SANTE (Static ANalysis and TEsting), implementing a combination of static analysis and structural program tetsting for detection of run-time errors in C programs. First, a static analysis tool (Frama-C) is called to generate alarms when it cannot ensure the absence of run-time errors. Second, these alarms guide a structural test generation tool (PathCrawler) trying to confirm alarms by activating bugs on some test cases. Our experiments on real-life software show that this combination can outperform the use of each technique independently

Slicing for Java Program: A Preliminary Study

Program slicing is a technique that proposed to help in understanding the program code. After several decades, the technique has been derived into several other techniques and proposed to be applied in many fields such as debugging, program comprehension, software measurement, testing and maintenance. The application of program slicing sometimes specifies for certain programming language such as C and Java. This paper will discuss existing program slicing techniques that were proposed focusing on the Java programming language

Detecting Dissimilar Classes of Source Code Defects

Software maintenance accounts for the most part of the software development cost and efforts, with its major activities focused on the detection, location, analysis and removal of defects present in the software. Although software defects can be originated, and be present, at any phase of the software development life-cycle, implementation (i.e., source code) contains more than three-fourths of the total defects. Due to the diverse nature of the defects, their detection and analysis activities have to be carried out by equally diverse tools, often necessitating the application of multiple tools for reasonable defect coverage that directly increases maintenance overhead. Unified detection tools are known to combine different specialized techniques into a single and massive core, resulting in operational difficulty and maintenance cost increment. The objective of this research was to search for a technique that can detect dissimilar defects using a simplified model and a single methodology, both of which should contribute in creating an easy-to-acquire solution. Following this goal, a ‘Supervised Automation Framework’ named FlexTax was developed for semi-automatic defect mapping and taxonomy generation, which was then applied on a large-scale real-world defect dataset to generate a comprehensive Defect Taxonomy that was verified using machine learning classifiers and manual verification. This Taxonomy, along with an extensive literature survey, was used for comprehension of the properties of different classes of defects, and for developing Defect Similarity Metrics. The Taxonomy, and the Similarity Metrics were then used to develop a defect detection model and associated techniques, collectively named Symbolic Range Tuple Analysis, or SRTA. SRTA relies on Symbolic Analysis, Path Summarization and Range Propagation to detect dissimilar classes of defects using a simplified set of operations. To verify the effectiveness of the technique, SRTA was evaluated by processing multiple real-world open-source systems, by direct comparison with three state-of-the-art tools, by a controlled experiment, by using an established Benchmark, by comparison with other tools through secondary data, and by a large-scale fault-injection experiment conducted using a Mutation-Injection Framework, which relied on the taxonomy developed earlier for the definition of mutation rules. Experimental results confirmed SRTA’s practicality, generality, scalability and accuracy, and proved SRTA’s applicability as a new Defect Detection Technique