C. Del Grosso

Bio: C. Del Grosso is an academic researcher from University of Sannio. The author has contributed to research in topics: Software system & Software maintenance. The author has an hindex of 3, co-authored 3 publications receiving 109 citations.

An approach for mining services in database oriented applications

Abstract: The diffusion of service oriented architectures is slowed down by the lack of enough services available for satisfying service integrator needs. Nevertheless, many features desired by service integrators have already been implemented in existing software systems. To this aim, approaches able to identify potential services into a legacy system source code are highly desirable. This paper proposes an approach to identify, from database-oriented applications, pieces of functionality to be potentially exported as services. The identification is performed by clustering, through formal concept analysis, queries dynamically extracted by observing interactions between the application and the database. The approach has been assessed by identifying potential services in two Java software systems

Smart Formatter: Learning Coding Style from Existing Source Code

Abstract: The quality of identifiers, the coding style and formatting are important aspects that influence program understandings and maintenance. This is confirmed by the presence of several approaches and tools aimed at supporting and improving the source code quality. This paper describes a tool named Smart Formatter that allows programmers to learn coding style rules from existing source code, and apply these rules to the code under development.

Search-based software engineering: Trends, techniques and applications

Abstract: In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

The Current State and Future of Search Based Software Engineering

Abstract: This paper describes work on the application of optimization techniques in software engineering. These optimization techniques come from the operations research and metaheuristic computation research communities. The paper briefly reviews widely used optimization techniques and the key ingredients required for their successful application to software engineering, providing an overview of existing results in eight software engineering application domains. The paper also describes the benefits that are likely to accrue from the growing body of work in this area and provides a set of open problems, challenges and areas for future work.

VUzzer: Application-aware Evolutionary Fuzzing.

Abstract: See, stats, and : https : / / www . researchgate . net / publication / 311886374 VUzzer : Application - aware Conference DOI : 10 . 14722 / ndss . 2017 . 23404 CITATIONS 0 READS 17 6 , including : Some : Systems Sanjay Vrije , Amsterdam , Netherlands 38 SEE Ashish International 1 SEE Cristiano VU 51 SEE Herbert VU 163 , 836 SEE All . The . All - text and , letting . Abstract—Fuzzing is an effective software testing technique to find bugs . Given the size and complexity of real - world applications , modern fuzzers tend to be either scalable , but not effective in exploring bugs that lie deeper in the execution , or capable of penetrating deeper in the application , but not scalable . In this paper , we present an application - aware evolutionary fuzzing strategy that does not require any prior knowledge of the application or input format . In order to maximize coverage and explore deeper paths , we leverage control - and data - flow features based on static and dynamic analysis to infer fundamental prop - erties of the application . This enables much faster generation of interesting inputs compared to an application - agnostic approach . We implement our fuzzing strategy in VUzzer and evaluate it on three different datasets : DARPA Grand Challenge binaries (CGC) , a set of real - world applications (binary input parsers) , and the recently released LAVA dataset . On all of these datasets , VUzzer yields significantly better results than state - of - the - art fuzzers , by quickly finding several existing and new bugs .

A systematic review of search-based testing for non-functional system properties

Abstract: Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.

Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications

Abstract: In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.