Search-based amorphous slicing

Amorphous slicing is an automated source code extraction technique with applications in many areas of software engineering, including comprehension, reuse, testing and reverse engineering. Algorithms for syntax-preserving slicing are well established, but amorphous slicing is harder because it requires arbitrary transformation; finding good general purpose amorphous slicing algorithms therefore remains as hard as general program transformation. In this paper we show how amorphous slices can be computed using search techniques. The paper presents results from a set of experiments designed to explore the application of genetic algorithms, hill climbing, random search and systematic search to a set of six subject programs. As a benchmark, the results are compared to those from an existing analytical algorithm for amorphous slicing, which was written specifically to perform well with the sorts of program under consideration. The results, while tentative at this stage, do give grounds for optimism. The search techniques proved able to reduce the size of the programs under consideration in all cases, sometimes equaling the performance of the specifically-tailored analytic algorithm. In one case, the search techniques performed better, highlighting a fault in the existing algorith

Automated Search for Good Coverage Criteria: Moving from Code Coverage to Fault Coverage Through Search-Based Software Engineering

We propose to use Search-Based Software Engineering to automatically evolve coverage criteria that are well correlated with fault revelation, through the use of existing fault databases. We explain how problems of bloat and overfitting can be ameliorated in our approach, and show how this new method will yield insight into faults — as well as better guidance for Search-Based Software Testing

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Automated migration of build scripts using dynamic analysis and search-based refactoring

The efficiency of a build system is an important factor for developer productivity. As a result, developer teams have been increasingly adopting new build systems that allow higher build parallelization. However, migrating the existing legacy build scripts to new build systems is a tedious and error-prone process. Unfortunately, there is insufficient support for automated migration of build scripts, making the migration more problematic. We propose the first dynamic approach for automated migration of build scripts to new build systems. Our approach works in two phases. First, from a set of execution traces, we synthesize build scripts that accurately capture the intent of the original build. The synthesized build scripts are typically long and hard to maintain. Second, we apply refactorings that raise the abstraction level of the synthesized scripts (e.g., introduce functions for similar fragments). As different refactoring sequences may lead to different build scripts, we use a search-based approach that explores various sequences to identify the best (e.g., shortest) build script. We optimize search-based refactoring with partial-order reduction to faster explore refactoring sequences. We implemented the proposed two phase migration approach in a tool called METAMORPHOSIS that has been recently used at Microsoft

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.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 article 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.EPSRC and E

A Weighted Grid for Measuring Program Robustness

Robustness is a key issue for all the programs, especially safety critical ones. In the literature, Program Robustness is defined as “the degree to which a system or component can function correctly in the presence of invalid input or stressful environment” (IEEE 1990). Robustness measurement is the value that reflects the Robustness Degree of the program. In this thesis, a new Robustness measurement technique; the Robustness Grid, is introduced. The Robustness Grid measures the Robustness Degree for programs, C programs in this instance, using a relative scale. It allows programmers to find the program’s vulnerable points, repair them, and avoid similar mistakes in the future. The Robustness Grid is a table that contains Language rules, which is classified into categories with respect to the program’s function names, and calculates the robustness degree. The Motor Industry Software Reliability Association (MISRA) C language rules with the Clause Program Slicing technique will be the basis for the robustness measurement mechanism. In the Robustness Grid, for every MISRA rule, a score will be given to a function every time it satisfies or violates a rule. Furthermore, Clause program slicing will be used to weight every MISRA rule to illustrate its importance in the program. The Robustness Grid shows how much each part of the program is robust and effective, and assists developers to measure and evaluate the robustness degree for each part of a program. Overall, the Robustness Grid is a new technique that measures the robustness of C programs using MISRA C rules and Clause program slicing. The Robustness Grid shows the program robustness degree and the importance of each part of the program. An evaluation of the Robustness Grid is performed to show that it offers new measurements that were not provided before

Genetic Improvement of Software: From Program Landscapes to the Automatic Improvement of a Live System

In today’s technology driven society, software is becoming increasingly important in more areas of our lives. The domain of software extends beyond the obvious domain of computers, tablets, and mobile phones. Smart devices and the internet-of-things have inspired the integra- tion of digital and computational technology into objects that some of us would never have guessed could be possible or even necessary. Fridges and freezers connected to social media sites, a toaster activated with a mobile phone, physical buttons for shopping, and verbally asking smart speakers to order a meal to be delivered. This is the world we live in and it is an exciting time for software engineers and computer scientists. The sheer volume of code that is currently in use has long since outgrown beyond the point of any hope for proper manual maintenance. The rate of which mobile application stores such as Google’s and Apple’s have expanded is astounding. The research presented here aims to shed a light on an emerging field of research, called Genetic Improvement ( GI ) of software. It is a methodology to change program code to improve existing software. This thesis details a framework for GI that is then applied to explore fitness landscape of bug fixing Python software, reduce execution time in a C ++ program, and integrated into a live system. We show that software is generally not fragile and although fitness landscapes for GI are flat they are not impossible to search in. This conclusion applies equally to bug fixing in small programs as well as execution time improvements. The framework’s application is shown to be transportable between programming languages with minimal effort. Additionally, it can be easily integrated into a system that runs a live web service. The work within this thesis was funded by EPSRC grant EP/J017515/1 through the DAASE project

A Mono- and Multi-objective Approach for Recommending Software Refactoring

Les systèmes logiciels sont devenus de plus en plus répondus et importants dans notre société. Ainsi, il y a un besoin constant de logiciels de haute qualité. Pour améliorer la qualité de logiciels, l’une des techniques les plus utilisées est le refactoring qui sert à améliorer la structure d'un programme tout en préservant son comportement externe. Le refactoring promet, s'il est appliqué convenablement, à améliorer la compréhensibilité, la maintenabilité et l'extensibilité du logiciel tout en améliorant la productivité des programmeurs. En général, le refactoring pourra s’appliquer au niveau de spécification, conception ou code. Cette thèse porte sur l'automatisation de processus de recommandation de refactoring, au niveau code, s’appliquant en deux étapes principales: 1) la détection des fragments de code qui devraient être améliorés (e.g., les défauts de conception), et 2) l'identification des solutions de refactoring à appliquer. Pour la première étape, nous traduisons des régularités qui peuvent être trouvés dans des exemples de défauts de conception. Nous utilisons un algorithme génétique pour générer automatiquement des règles de détection à partir des exemples de défauts. Pour la deuxième étape, nous introduisons une approche se basant sur une recherche heuristique. Le processus consiste à trouver la séquence optimale d'opérations de refactoring permettant d'améliorer la qualité du logiciel en minimisant le nombre de défauts tout en priorisant les instances les plus critiques. De plus, nous explorons d'autres objectifs à optimiser: le nombre de changements requis pour appliquer la solution de refactoring, la préservation de la sémantique, et la consistance avec l’historique de changements. Ainsi, réduire le nombre de changements permets de garder autant que possible avec la conception initiale. La préservation de la sémantique assure que le programme restructuré est sémantiquement cohérent. De plus, nous utilisons l'historique de changement pour suggérer de nouveaux refactorings dans des contextes similaires. En outre, nous introduisons une approche multi-objective pour améliorer les attributs de qualité du logiciel (la flexibilité, la maintenabilité, etc.), fixer les « mauvaises » pratiques de conception (défauts de conception), tout en introduisant les « bonnes » pratiques de conception (patrons de conception).Software systems have become prevalent and important in our society. There is a constant need for high-quality software. Hence, to improve software quality, one of the most-used techniques is the refactoring which improves design structure while preserving the external behavior. Refactoring has promised, if applied well, to improve software readability, maintainability and extendibility while increasing the speed at which programmers can write and maintain their code. In general, refactoring can be performed in various levels such as the requirement, design, or code level. In this thesis, we mainly focus on the source code level where automated refactoring recommendation can be performed through two main steps: 1) detection of code fragments that need to be improved/fixed (e.g., code-smells), and 2) identification of refactoring solutions to achieve this goal. For the code-smells identification step, we translate regularities that can be found in such code-smell examples into detection rules. To this end, we use genetic programming to automatically generate detection rules from examples of code-smells. For the refactoring identification step, a search-based approach is used. The process aims at finding the optimal sequence of refactoring operations that improve software quality by minimizing the number of detected code-smells while prioritizing the most critical ones. In addition, we explore other objectives to optimize using a multi-objective approach: the code changes needed to apply refactorings, semantics preservation, and the consistency with development change history. Hence, reducing code changes allows us to keep as much as possible the initial design. On the other hand, semantics preservation insures that the refactored program is semantically coherent, and that it models correctly the domain-semantics. Indeed, we use knowledge from historical code change to suggest new refactorings in similar contexts. Furthermore, we introduce a novel multi-objective approach to improve software quality attributes (i.e., flexibility, maintainability, etc.), fix “bad” design practices (i.e., code-smells) while promoting “good” design practices (i.e., design patterns)

Automatic software generation and improvement through search based techniques

Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis we also give the important contribution of defining a first theoretical foundation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo