Search results

Now showing 1 - 3 of 3
  • Publication
    MISIM: A Novel Code Similarity System
    (2020-06-01) Ye, Fangke; Zhou, Shengtian; Venkat, Anand; Marcus, Ryan; Tatbul, Nesime; Tithi, Jesmin J; Hasabnis, Niranjan; Petersen, Paul; Mattson, Timothy; Kraska, Tim; Dubey, Pradeep; Gottschlich, Justin E; Gottschlich, Justin E
    Code similarity systems are integral to a range of applications from code recommendation to automated software defect correction. We argue that code similarity is now a first-order problem that must be solved. To begin to address this, we present machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware semantic structure, which is designed to aid in lifting semantic meaning from code syntax. Second, MISIM provides a neural-based code similarity scoring algorithm, which can be implemented with various neural network architectures with learned parameters. We compare MISIM to three state-of-the-art code similarity systems: (i) code2vec, (ii) Neural Code Comprehension, and (iii) Aroma. In our experimental evaluation across 328,155 programs (over 18 million lines of code), MISIM has 1.5x to 43.4x better accuracy than all three systems.
  • Publication
    The Three Pillars of Machine Programming
    (2018-01-01) Gottschlich, Justin E; Gottschlich, Justin E; Solar-Lezama, Armando; Tatbul, Nesime; Carbin, Michael; Rinard, Martin; Barzilay, Regina; Amarasinghe, Saman; Tenenbaum, Joshua B; Mattson, Timothy
    In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are:(i) intention,(ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.
  • Publication
    A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
    (2019-01-01) Gottschlich, Justin E; Gottschlich, Justin E; Tatbul, Nesime; Turek, Javier S; Mattson, Timothy; Muzahid, Abdullah
    The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf–a novel approach to automate regression testing that utilizes three core techniques:(i) zero-positive learning,(ii) autoencoders, and (iii) hardware telemetry. We demonstrate AutoPerf’s generality and efficacy against 3 types of performance regressions across 10 real performance bugs in 7 benchmark and open-source programs. On average, AutoPerf exhibits 4% profiling overhead and accurately diagnoses more performance bugs than prior state-of-the-art approaches. Thus far, AutoPerf has produced no false negatives.