Abstract: This disclosure relates generally to method and system for incremental functional approach-based dataflow analysis. Static dataflow analysis can take hours to days depending on size and complexity of the code. In today's agile development environment faster analysis is required which can handle incremental changes to the code in an efficient manner. The method includes by performing a static dataflow analysis over a set of functions of a source code. Further, obtains a set of impacted functions from the source code and executes a dataflow analysis over the set of impacted functions of the source code. The method performs an incremental functional approach-based dataflow analysis over the set of impacted functions including an incremental bottom-up analysis and an incremental top-down analysis. The method efficiently updates results of dataflow analysis in response to incremental changes which is fast and scalable and minimizes the number of procedures by comparing summaries across the versions.

Abstract: Modern software verification tools are moving towards incremental verification of program properties to ensure safety of evolving software systems. However, not every change in the program impacts verification outcome of program properties. Moreover, analyzing these irrelevant changes adds to cost of incremental verification. A system and method have been provided for identifying relevant changes for incremental verification of evolving software. The disclosure provides a light-weight pre-analysis phase of identifying relevant changes that can lead to improvement in efficiency of all existing incremental verification techniques. To identify relevant changes, the system provides a relevant change identification technique (RCIT) with respect to program properties. The RCIT uses a variant of the strongly live variables (SLV) analysis to compute variables that are influencing the verification outcome of program properties. These variables are referred as value influencing variables (VIVs).

Abstract: For performing incremental analysis, analysis tools may reuse analysis information computed earlier for intermediate representation (IR) objects that remain unchanged between versions of program. But identification numbers (IDs) of unchanged IR objects change over versions due to changes with each run of incremental analysis, thereby making it difficult to reuse analysis information as tools require IDs of unchanged IR objects from previous versions. Present disclosure provides method and system for mapping IR objects for facilitating incremental analysis. The system first extracts attributes associated with each IR object present in previous version to create data structure for storing IR objects. The system then determines unchanged lines between versions to create line number map. Thereafter, system uses line number map to create ID-based IR mapping between ID of each unchanged IR object present in each unchanged line in current version and ID of each IR object stored in data structure.

Abstract: This disclosure relates generally to method and system for identifying static analysis alarms based on semantics of changed source code. The disclosed technique is integrated in the proprietary static analysis tool that identifies semantics of the change and reports only impacted alarms. The method receives source code and a property over variables to be verified for identifying one or more impacted alarms. Further, an incremental analysis based on the one or more change program points are performed to mark one or more impacted functions in the current version of the source code and then generating a data flow analysis (DFA) and a program dependence graph (PDG) for the one or more impacted functions. Further, a change-based alarm identification technique is utilized for the one or more impacted static analysis alarms from the one or more impacted functions in the current version of source code based on semantics of change.

Abstract: This disclosure relates generally to data structure abstraction, and more particularly to method and system for data structure abstraction for model checking. In one embodiment, the method includes identifying data structure accesses in the source code. Loops are identified in the data structure accesses, and loop-types are identified in the loops. An abstracted code is generated based on the loop types for abstracting the data structure. Abstracting the data structure includes, for each loop, replacing the data structure accesses by one of a corresponding representative element and a non-deterministic value in the loop body of said loop based on elements accessed, and eliminating loop control statement of said loop operating on elements of data structure based on loop type of said loop, and adding a plurality of non-array assignments at a start and after the loop body of the source code. The abstracted code is provided for the model checking.

Abstract: This disclosure relates generally to data structure abstraction, and more particularly to method and system for data structure abstraction for model checking. In one embodiment, the method includes identifying data structure accesses in the source code. Loops are identified in the data structure accesses, and loop-types are identified in the loops. An abstracted code is generated based on the loop types for abstracting the data structure. Abstracting the data structure includes, for each loop, replacing the data structure accesses by one of a corresponding representative element and a non-deterministic value in the loop body of said loop based on elements accessed, and eliminating loop control statement of said loop operating on elements of data structure based on loop type of said loop, and adding a plurality of non-array assignments at a start and after the loop body of the source code. The abstracted code is provided for the model checking.