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Along with the growing number of students with disabilities in higher education comes an opportunity to explore the difficulties they experience, especially in the post-pandemic era, as well as how to better support them, thus making post-secondary education more inclusive. A considerable amount of research has been done in providing accommodation for students with physical disabilities, but other hindrances to accessibility such as mental health conditions are prone to be overlooked, perhaps in part due to the stigmatization and subjective invisibility of this topic, specifically in rigorous, competitive fields such as Computer Science (CS). In order to bridge this gap, we conducted a nationwide survey in which 53 undergraduate CS students who identify as living with a mental health condition shared their experiences in their CS courses, instructor and TA office hours, interactions with other students, and the rest of the field. This paper summarizes the most common negative and positive experiences, as well as respondents' recommendations for CS instructors, including recognizing these students' struggles, making themselves approachable, and providing flexible formats of lectures and office hours. The results of this study provide a glimpse of the academic lives of CS students living with mental health conditions, so that CS instructors could foster a more inclusive environment by supporting more students in their paths of pursuing higher education. 

For some applications, it is impossible or impractical to know what the correct output should be for an arbitrary input, making testing difficult. Many machine-learning applications for “big data”, bioinformatics and cyberphysical systems fall in this scope: they do not have a test oracle. Metamorphic Testing, a simple testing technique that does not require a test oracle, has been shown to be effective for testing such applications. We present Metamorphic Runtime Checking, a novel approach that conducts metamorphic testing of both the entire application and individual functions during a program’s execution. We have applied Metamorphic Runtime Checking to 9 machine-learning applications, finding it to be on average 170% more effective than traditional metamorphic testing at only the full application level. 

Metamorphic testing is an advanced technique to test programs without a true test oracle such as machine learning applications. Because these programs have no general oracle to identify their correctness, traditional testing techniques such as unit testing may not be helpful for developers to detect potential bugs. This paper presents a novel system, KABU, which can dynamically infer properties of methods' states in programs that describe the characteristics of a method before and after transforming its input. These Metamorphic Properties (MPs) are pivotal to detecting potential bugs in programs without test oracles, but most previous work relies solely on human effort to identify them and only considers MPs between input parameters and output result (return value) of a program or method. This paper also proposes a testing concept, Metamorphic Differential Testing (MDT). By detecting different sets of MPs between different versions for the same method, KABU reports potential bugs for human review. We have performed a preliminary evaluation of KABU by comparing the MPs detected by humans with the MPs detected by KABU. Our preliminary results are promising: KABU can find more MPs than human developers, and MDT is effective at detecting function changes in methods.