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Full-Text Articles in Physical Sciences and Mathematics
Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Anita Raja
Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Anita Raja
Publications and Research
Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …
A Tool For Rejuvenating Feature Logging Levels Via Git Histories And Degree Of Interest, Yiming Tang, Allan Spektor, Raffi T. Khatchadourian, Mehdi Bagherzadeh
A Tool For Rejuvenating Feature Logging Levels Via Git Histories And Degree Of Interest, Yiming Tang, Allan Spektor, Raffi T. Khatchadourian, Mehdi Bagherzadeh
Publications and Research
Logging is a significant programming practice. Due to the highly transactional nature of modern software applications, a massive amount of logs are generated every day, which may overwhelm developers. Logging information overload can be dangerous to software applications. Using log levels, developers can print the useful information while hiding the verbose logs during software runtime. As software evolves, the log levels of logging statements associated with the surrounding software feature implementation may also need to be altered. Maintaining log levels necessitates a significant amount of manual effort. In this paper, we demonstrate an automated approach that can rejuvenate feature log …
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
Publications and Research
Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …
Automated Evolution Of Feature Logging Statement Levels Using Git Histories And Degree Of Interest, Yiming Tang, Allan Spektor, Raffi Khatchadourian, Mehdi Bagherzadeh
Automated Evolution Of Feature Logging Statement Levels Using Git Histories And Degree Of Interest, Yiming Tang, Allan Spektor, Raffi Khatchadourian, Mehdi Bagherzadeh
Publications and Research
Logging—used for system events and security breaches to more informational yet essential aspects of software features—is pervasive. Given the high transactionality of today’s software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating …
Detecting Broken Pointcuts Using Structural Commonality And Degree Of Interest, Raffi T. Khatchadourian, Awais Rashd, Hidehiko Masuhara, Takuya Watanabe
Detecting Broken Pointcuts Using Structural Commonality And Degree Of Interest, Raffi T. Khatchadourian, Awais Rashd, Hidehiko Masuhara, Takuya Watanabe
Publications and Research
Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken due to base-code changes is daunting, especially in large and complex systems. We present an automated approach that recommends pointcuts that are likely to require modification due to a certain base-code change and ones that do not. Our hypothesis is that join points selected by a pointcut exhibit common structural characteristics. Patterns describing such commonalities recommend pointcuts that have potentially broken to the developer. The approach …
Fraglight: Shedding Light On Broken Pointcuts In Evolving Aspect-Oriented Software, Raffi T. Khatchadourian, Awais Rashid, Hidehiko Masuhara, Takuya Watanabe
Fraglight: Shedding Light On Broken Pointcuts In Evolving Aspect-Oriented Software, Raffi T. Khatchadourian, Awais Rashid, Hidehiko Masuhara, Takuya Watanabe
Publications and Research
Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken due to base-code changes is daunting, especially in large and complex systems. We demonstrate an automated tool called FRAGLIGHT that recommends a set of pointcuts that are likely to require modification due to a certain base-code change. The underlying approach is rooted in harnessing unique and arbitrarily deep structural commonality between program elements corresponding to join points selected by a pointcut in a particular software version. …
Enforcing Behavioral Constraints In Evolving Aspect-Oriented Programs, Raffi T. Khatchadourian, Johan Dovland, Neelam Soundarajan
Enforcing Behavioral Constraints In Evolving Aspect-Oriented Programs, Raffi T. Khatchadourian, Johan Dovland, Neelam Soundarajan
Publications and Research
Reasoning, specification, and verification of Aspect-Oriented (AO) programs presents unique challenges especially as such programs evolve over time. Components, base-code and aspects alike, may be easily added, removed, interchanged, or presently unavailable at unpredictable frequencies. Consequently, modular reasoning of such programs is highly attractive as it enables tractable evolution, otherwise necessitating that the entire program be reexamined each time a component is changed. It is well known, however, that modular reasoning about AO programs is difficult. In this paper, we present our ongoing work in constructing a rely-guarantee style reasoning system for the Aspect-Oriented Programming (AOP) paradigm, adopting a trace-based …