Improving Automated Software Testing while re-engineering legacy systems in the absence of documentation

Legacy software systems are essential assets that contain an organizations' valuable business logic. Because of outdated technologies and methods used in these systems, they are challenging to maintain and expand. Therefore, organizations need to decide whether to redevelop or re-engineer the legacy system. Although in most cases, re-engineering is the safer and less expensive choice, it has risks such as failure to meet the expected quality and delays due to testing blockades. These risks are even more severe when the legacy system does not have adequate documentation. A comprehensive testing strategy, which includes automated tests and reliable test cases, can substantially reduce the risks. To mitigate the hazards associated with re-engineering, we have conducted three studies in this thesis to improve the testing process. Our rst study introduces a new testing model for the re-engineering process and investigates test automation solutions to detect defects in the early re-engineering stages. We implemented this model on the Cold Region Hydrological Model (CRHM) application and discovered bugs that would not likely have been found manually. Although this approach helped us discover great numbers of software defects, designing test cases is very time-consuming due to the lack of documentation, especially for large systems. Therefore, in our second study, we investigated an approach to generate test cases from user footprints automatically. To do this, we extended an existing tool to collect user actions and legacy system reactions, including database and le system changes. Then we analyzed the data based on the order of user actions and time of them and generated human-readable test cases. Our evaluation shows that this approach can detect more bugs than other existing tools. Moreover, the test cases generated using this approach contain detailed oracles that make them suitable for both black-box and white-box testing. Many scienti c legacy systems such as CRHM are data-driven; they take large amounts of data as input and produce massive data after applying mathematical models. Applying test cases and nding bugs is more demanding when we are dealing with large amounts of data. Hence in our third study, we created a comparative visualization tool (ComVis) to compare a legacy system's output after each change. Visualization helps testers to nd data issues resulting from newly introduced bugs. Twenty participants took part in a user study in which they were asked to nd data issued using ComVis and embedded CRHM visualization tool. Our user study shows that ComVis can nd 51% more data issues than embedded visualization tools in the legacy system can. Also, results from the NASA-TLX assessment and thematic analysis of open-ended questions about each task show users prefer to use ComVis over the built-in visualization tool. We believe our introduced approaches and developed systems will signi cantly reduce the risks associated with the re-engineering process. i

From experiment to design – fault characterization and detection in parallel computer systems using computational accelerators

This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of program states that included dynamically allocated memory (to be spatially comprehensive). In GPUs, we used fault injection studies to demonstrate the importance of detecting silent data corruption (SDC) errors that are mainly due to the lack of fine-grained protections and the massive use of fault-insensitive data. This dissertation also presents transparent fault tolerance frameworks and techniques that are directly applicable to hybrid computers built using only commercial off-the-shelf hardware components. This dissertation shows that by developing understanding of the failure characteristics and error propagation paths of target programs, we were able to create fault tolerance frameworks and techniques that can quickly detect and recover from hardware faults with low performance and hardware overheads