Improving the Quality Assurance of Machine-Learning Software Applications

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Machine learning (ML) is increasingly deployed in large-scale and critical systems thanks to recent breakthroughs in deep learning and reinforcement learning. We are now using software applications powered by ML in critical aspects of our daily lives; from finance, energy, to health and transportation. The economic benefits of Machine-Learning Software Applications (MLSA) and Artificial Intelligence (AI) in general is forecast to surpass USD 8.81 Billion by 2022. However, ensuring the quality assurance of MLSA is still very challenging as evidenced by the recent deadly incident caused by the $47-million Michigan Integrated Data Automated System (MiDAS), or the Uber's self-driving car that ran into a pedestrian even though the car's sensors detected her presence. The MLSA running the Uber's car reportedly considered the detection of the pedestrian as a "false positive”. The main reason behind the difficulty to ensure quality in MLSA is the shift in the development paradigm induced by ML and AI. Traditionally, software systems are constructed deductively, by writing down the rules that govern the behavior of the system as program code. However, with ML, these rules are inferred from training data (i.e., the requirements are generated inductively). This paradigm shift in application development makes it difficult to reason about the behavior of software systems with ML components, resulting in systems that are intrinsically challenging to test and verify. A defect in a MLSA may come from its training data, program code, execution environment, or third-party frameworks (e.g., TensorFlow). Also, ML models must be retrained and evolved constantly to cope with changes in users' behaviors, model drift, or adversarial interactions for example, hence the necessity to architect them in a way that minimizes the cost of these frequent models changes on their overall maintenance and evolution. Current existing software development techniques must be revisited and adapted to this new reality. ***The goal of this research program is to develop techniques and tools to support quality assurance activities for MLSA systems, given that they do not have (complete) specifications or even source code corresponding to some of their critical behaviors (some MLSA rely on proprietary third-party libraries like Intel Math Kernel Library for many critical operations). Through this research program, my students and I will identify good and bad development practices that can impede the maintenance and the reliability of MLSA. I will also develop techniques and tools to help developers detect and correct errors in MLSA, both at design and implementation levels. **