Specialising Software for Different Downstream Applications Using Genetic Improvement and Code Transplantation

OAPA Genetic improvement uses computational search to improve existing software while retaining its partial functionality. Genetic improvement has previously been concerned with improving a system with respect to all possible usage scenarios. In this paper, we show how genetic improvement can also be used to achieve specialisation to a specific set of usage scenarios. We use genetic improvement to evolve faster versions of a C++ program, a Boolean satisfiability solver called MiniSAT, specialising it for three applications. Our specialised solvers achieve between 4% and 36% execution time improvement, which is commensurate with efficiency gains achievable using human expert optimisation for the general solver. We also use genetic improvement to evolve faster versions of an image processing tool called ImageMagick, utilising code from GraphicsMagick, another image processing tool which was forked from it. We specialise the format conversion functionality to black & amp; white images and colour images only. Our specialised versions achieve up to 3% execution time improvement

Search-based Unit Test Generation for Evolving Software

Search-based software testing has been successfully applied to generate unit test cases for object-oriented software. Typically, in search-based test generation approaches, evolutionary search algorithms are guided by code coverage criteria such as branch coverage to generate tests for individual coverage objectives. Although it has been shown that this approach can be effective, there remain fundamental open questions. In particular, which criteria should test generation use in order to produce the best test suites? Which evolutionary algorithms are more effective at generating test cases with high coverage? How to scale up search-based unit test generation to software projects consisting of large numbers of components, evolving and changing frequently over time? As a result, the applicability of search-based test generation techniques in practice is still fundamentally limited. In order to answer these fundamental questions, we investigate the following improvements to search-based testing. First, we propose the simultaneous optimisation of several coverage criteria at the same time using an evolutionary algorithm, rather than optimising for individual criteria. We then perform an empirical evaluation of different evolutionary algorithms to understand the influence of each one on the test optimisation problem. We then extend a coverage-based test generation with a non-functional criterion to increase the likelihood of detecting faults as well as helping developers to identify the locations of the faults. Finally, we propose several strategies and tools to efficiently apply search-based test generation techniques in large and evolving software projects. Our results show that, overall, the optimisation of several coverage criteria is efficient, there is indeed an evolutionary algorithm that clearly works better for test generation problem than others, the extended coverage-based test generation is effective at revealing and localising faults, and our proposed strategies, specifically designed to test entire software projects in a continuous way, improve efficiency and lead to higher code coverage. Consequently, the techniques and toolset presented in this thesis - which provides support to all contributions here described - brings search-based software testing one step closer to practical usage, by equipping software engineers with the state of the art in automated test generation

Information Retrieval and Spectrum Based Bug Localization: Better Together

Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual infor-mation in bug reports, while spectrum-based techniques pro-cess program spectra (i.e., a record of which program el-ements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only con-sider one source of information, either bug reports or pro-gram spectra, which is not optimal. To deal with the limita-tion of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature loca-tion methods that are adapted for bug localization. Experi-ments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80 % in terms of number of bugs successfully localized when a developer in

From start-ups to scale-ups: Opportunities and open problems for static and dynamic program analysis

This paper describes some of the challenges and opportunities when deploying static and dynamic analysis at scale, drawing on the authors' experience with the Infer and Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, impacting billions of people worldwide. The paper identifies open problems that have yet to receive significant attention from the scientific community, yet which have potential for profound real world impact, formulating these as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects

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

Exact analysis for requirements selection and optimisation

Requirements engineering is the prerequisite of software engineering, and plays a crit- ically strategic role in the success of software development. Insufficient management of uncertainty in the requirements engineering process has been recognised as a key reason for software project failure. The essence of uncertainty may arise from partially observable, stochastic environments, or ignorance. To ease the impact of uncertainty in the software development process, it is important to provide techniques that explicitly manage uncertainty in requirements selection and optimisation. This thesis presents a decision support framework to exactly address the uncertainty in requirements selection and optimisation. Three types of uncertainty are managed. They are requirements uncertainty, algorithmic uncertainty, and uncertainty of resource constraints. Firstly, a probabilistic robust optimisation model is introduced to enable the manageability of requirements uncertainty. Requirements uncertainty is probabilis- tically simulated by Monte-Carlo Simulation and then formulated as one of the opti- misation objectives. Secondly, a probabilistic uncertainty analysis and a quantitative analysis sub-framework METRO is designed to cater for requirements selection deci- sion support under uncertainty. An exact Non-dominated Sorting Conflict Graph based Dynamic Programming algorithm lies at the heart of METRO to guarantee the elim- ination of algorithmic uncertainty and the discovery of guaranteed optimal solutions. Consequently, any information loss due to algorithmic uncertainty can be completely avoided. Moreover, a data analytic approach is integrated in METRO to help the deci- sion maker to understand the remaining requirements uncertainty propagation through- out the requirements selection process, and to interpret the analysis results. Finally, a more generic exact multi-objective integrated release and schedule planning approach iRASPA is introduced to holistically manage the uncertainty of resource constraints for requirements selection and optimisation. Software release and schedule plans are inte- grated into a single activity and solved simultaneously. Accordingly, a more advanced globally optimal result can be produced by accommodating and managing the inherent additional uncertainty due to resource constraints as well as that due to requirements. To settle the algorithmic uncertainty problem and guarantee the exactness of results, an ε-constraint Quadratic Programming approach is used in iRASPA

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.Comment: journal submission, 34 pages, 8 figure