Organizing the Technical Debt Landscape

To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subjec

Combining Solution Reuse and Bound Tightening for Efficient Analysis of Evolving Systems

Software engineers have long employed formal verification to ensure the safety and validity of their system designs. As the system changes—often via predictable, domain-specific operations—their models must also change, requiring system designers to repeatedly execute the same formal verification on similar system models. State-of-the-art formal verification techniques can be expensive at scale, the cost of which is multiplied by repeated analysis. This paper presents a novel analysis technique—implemented in a tool called SoRBoT—which can automatically determine domain-specific optimizations that can dramatically reduce the cost of repeatedly analyzing evolving systems. Different from all prior approaches, which focus on either tightening the bounds for analysis or reusing all or part of prior solutions, SoRBoT’s automated derivation of domain-specific optimizations combines the benefits of both solution reuse and bound tightening while avoiding the main pitfalls of each. We experimentally evaluate SoRBoT against state-of-the-art techniques for verifying evolving specifications, demonstrating that SoRBoT substantially exceeds the run time performance of those state-of-the-art techniques while introducing only a negligible overhead, in contrast to the expensive additional computations required by the state-of-the-art verification techniques

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

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

Improving System Reliability for Cyber-Physical Systems

Cyber-physical systems (CPS) are systems featuring a tight combination of, and coordination between, the system's computational and physical elements. Cyber-physical systems include systems ranging from critical infrastructure such as a power grid and transportation system to health and biomedical devices. System reliability, i.e., the ability of a system to perform its intended function under a given set of environmental and operational conditions for a given period of time, is a fundamental requirement of cyber-physical systems. An unreliable system often leads to disruption of service, financial cost and even loss of human life. An important and prevalent type of cyber-physical system meets the following criteria: processing large amounts of data; employing software as a system component; running online continuously; having operator-in-the-loop because of human judgment and an accountability requirement for safety critical systems. This thesis aims to improve system reliability for this type of cyber-physical system. To improve system reliability for this type of cyber-physical system, I present a system evaluation approach entitled automated online evaluation (AOE), which is a data-centric runtime monitoring and reliability evaluation approach that works in parallel with the cyber-physical system to conduct automated evaluation along the workflow of the system continuously using computational intelligence and self-tuning techniques and provide operator-in-the-loop feedback on reliability improvement. For example, abnormal input and output data at or between the multiple stages of the system can be detected and flagged through data quality analysis. As a result, alerts can be sent to the operator-in-the-loop. The operator can then take actions and make changes to the system based on the alerts in order to achieve minimal system downtime and increased system reliability. One technique used by the approach is data quality analysis using computational intelligence, which applies computational intelligence in evaluating data quality in an automated and efficient way in order to make sure the running system perform reliably as expected. Another technique used by the approach is self-tuning which automatically self-manages and self-configures the evaluation system to ensure that it adapts itself based on the changes in the system and feedback from the operator. To implement the proposed approach, I further present a system architecture called autonomic reliability improvement system (ARIS). This thesis investigates three hypotheses. First, I claim that the automated online evaluation empowered by data quality analysis using computational intelligence can effectively improve system reliability for cyber-physical systems in the domain of interest as indicated above. In order to prove this hypothesis, a prototype system needs to be developed and deployed in various cyber-physical systems while certain reliability metrics are required to measure the system reliability improvement quantitatively. Second, I claim that the self-tuning can effectively self-manage and self-configure the evaluation system based on the changes in the system and feedback from the operator-in-the-loop to improve system reliability. Third, I claim that the approach is efficient. It should not have a large impact on the overall system performance and introduce only minimal extra overhead to the cyberphysical system. Some performance metrics should be used to measure the efficiency and added overhead quantitatively. Additionally, in order to conduct efficient and cost-effective automated online evaluation for data-intensive CPS, which requires large volumes of data and devotes much of its processing time to I/O and data manipulation, this thesis presents COBRA, a cloud-based reliability assurance framework. COBRA provides automated multi-stage runtime reliability evaluation along the CPS workflow using data relocation services, a cloud data store, data quality analysis and process scheduling with self-tuning to achieve scalability, elasticity and efficiency. Finally, in order to provide a generic way to compare and benchmark system reliability for CPS and to extend the approach described above, this thesis presents FARE, a reliability benchmark framework that employs a CPS reliability model, a set of methods and metrics on evaluation environment selection, failure analysis, and reliability estimation. The main contributions of this thesis include validation of the above hypotheses and empirical studies of ARIS automated online evaluation system, COBRA cloud-based reliability assurance framework for data-intensive CPS, and FARE framework for benchmarking reliability of cyber-physical systems. This work has advanced the state of the art in the CPS reliability research, expanded the body of knowledge in this field, and provided some useful studies for further research