Testing is a crucial part of any software development process. Testing is also very expensive: Common estimations list the effort of software testing at 50% of the average budget. Recent studies suggest that 77% of the time that software developers spend with testing is used for reading tests. Tests are read when they are generated, when they are updated, fixed, or refactored, when they serve as API usage examples and specification, or during debugging. Reading and understanding tests can be challenging, and evidence suggests that, despite the popularity of unit testing frameworks and test-driven development, the majority of software developers do not practice testing actively. Automatically generated tests tend to be particularly unreadable, severely inhibiting the widespread use of automated test generation in practice. The effects of insufficient testing can be dramatic, with large economic damage, and the potential to harm people relying on software in safety critical applications. Our proposed solution to address this problem is to improve the effectiveness and efficiency of testing by improving the readability of tests. We will investigate which syntactic and semantic aspects make tests readable, such that we can make readability measurable by modelling it. This, in turn, will allow us to provide techniques that guide manual or automatic improvement of the readability of software tests. This is made possible by a unique combination of machine learning, crowd sourcing, and search-based testing techniques. The GReaTest project will provide tools to developers that help them to identify readability problems, to automatically improve readability, and to automatically generate readability optimised test suites. The importance of readability and the usefulness of readability improvement will be evaluated with a range of empirical studies in conjunction with our industrial collaborators Microsoft, Google, and Barclays, investigating the relation of test readability to fault finding effectiveness, developer productivity, and software quality. Automated analysis and optimisation of test readability is novel, and traditional analyses only focused on easily measurable program aspects, such as code coverage. Improving readability of software tests has a direct impact on industry, where testing is a major economic and technical factor: More readable tests will reduce the costs of testing and increase effectiveness, thus improving software quality. Readability optimisation will be a key enabler for automated test generation in practice. Once readability of software tests is understood, this opens the doors to a new research direction on analysis and improvement of other software artefacts based on human understanding and performance.

Testing is a crucial part of any software development process. Testing is also very expensive: Common estimations list the effort of software testing at 50% of the average budget. Recent studies suggest that 77% of the time that software developers spend with testing is used for reading tests. Tests are read when they are generated, when they are updated, fixed, or refactored, when they serve as API usage examples and specification, or during debugging. Reading and understanding tests can be challenging, and evidence suggests that, despite the popularity of unit testing frameworks and test-driven development, the majority of software developers do not practice testing actively. Automatically generated tests tend to be particularly unreadable, severely inhibiting the widespread use of automated test generation in practice. The effects of insufficient testing can be dramatic, with large economic damage, and the potential to harm people relying on software in safety critical applications. Our proposed solution to address this problem is to improve the effectiveness and efficiency of testing by improving the readability of tests. We will investigate which syntactic and semantic aspects make tests readable, such that we can make readability measurable by modelling it. This, in turn, will allow us to provide techniques that guide manual or automatic improvement of the readability of software tests. This is made possible by a unique combination of machine learning, crowd sourcing, and search-based testing techniques. The GReaTest project will provide tools to developers that help them to identify readability problems, to automatically improve readability, and to automatically generate readability optimised test suites. The importance of readability and the usefulness of readability improvement will be evaluated with a range of empirical studies in conjunction with our industrial collaborators Microsoft, Google, and Barclays, investigating the relation of test readability to fault finding effectiveness, developer productivity, and software quality. Automated analysis and optimisation of test readability is novel, and traditional analyses only focused on easily measurable program aspects, such as code coverage. Improving readability of software tests has a direct impact on industry, where testing is a major economic and technical factor: More readable tests will reduce the costs of testing and increase effectiveness, thus improving software quality. Readability optimisation will be a key enabler for automated test generation in practice. Once readability of software tests is understood, this opens the doors to a new research direction on analysis and improvement of other software artefacts based on human understanding and performance.

The financial services industry is at the forefront of the digital economy, and is crucial to the UK's, and especially London's, continuing social and economic prosperity. State-of-the-art Financial IT, Computational Finance and Financial Engineering (collectively Financial Computing) research is crucial to our international competitiveness in investment banking, investment funds or retail banking. Academically this DTC focuses on financial computing, as distinct from quantitative finance, already well resourced. Banks and funds view PhD students in science and engineering as an increasingly important and largely untapped talent pool; although one regrettably with little knowledge of finance. The Financial Services Skills Council notes that employers are placing increasing importance on high-level analytical skills, as well as their acute shortage, especially in the newly emerging areas that drive sector growth. This centre completely embraces the spirit of the Digital Economy programme. The proposed DTC is inherently multidisciplinary involving UCL Computer Science, one of the largest leading departments in its field in the UK, with LSE Finance and the London Business School; the two leading academic finance centres in the UK. Key to developing the financial services industry in the Digital Economy is the creation of a new cohort of researchers who have a strong research capability in IT and computation, but also understand finance and the needs of the wholesale financial services industry leading to early adoption of new financial information technology research.The research groups and centres that will participate in this DTC include worldclass groups at: UCL, such as the Software Systems Engineering Group and the Centre for Computational Statistics and Machine Learning, at LSE such as Financial Markets Group, and at the London Business School, including the Management Science and Operations and Finance Subject Areas. The total value of active grants currently held by the participating groups and centres exceeds 20 Million Pounds, and the number of currently registered PhD students exceeds 130. Collaborators in Statistics, Economics, Mathematics and Physics supplement the potential Supervisor pool.A great strength of this DTC proposal is our industry partners, which include: Abbey, Barclays, Barclays Capital, BNP Paribas, Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC, Lloyds TSB, Man Investments, Merrill Lynch, Morgan Stanley, Nomura, RBS and Thomson Reuters. Regarding training and supervision, each DTC PhD student will follow a personally tailored programme of postgraduate courses drawn from the partners covering financial IT, networks & communications, HCI, computational finance, financial engineering and business, supplemented by lectures from our industry partners: * A tailored educational programme comprising graduate-level courses from UCL, LSE and LBS. * An academic supervisor (from UCL, LSE or LBS) and an industrial advisor (a partner bank, fund or Reuters), and a programme of research covered by an MOU. * A research project in financial IT, computational finance or financial engineering. * Training in industry software, such as Reuters 3000 Xtra, through UCL's virtual training floor.* A substantial period of industrial placement as agreed between the academic and industrial supervisors.* A short period at a leading foreign academic centre