Search Results - General AND reference Cross-computing tools AND techniques Metrics

MLPerf Inference Benchmark by Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, Zhou, Yuchen

“…Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded…”
Get full text

Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques by Khan, Zanis Ali, Shin, Donghwan, Bianculli, Domenico, Briand, Lionel

“… While many techniques have been proposed in the literature, only two recent studies provide a comprehensive evaluation and comparison of the techniques using an established benchmark composed of real-world logs…”
Get full text

FAST approaches to scalable similarity-based test case prioritization by Miranda, Breno, Cruciani, Emilio, Verdecchia, Roberto, Bertolino, Antonia

“… We introduce the FAST family of test case prioritization techniques that radically changes this landscape by borrowing algorithms commonly exploited in the big data domain to find similar items…”
Get full text

Can Code Metrics Enhance Documentation Generation for Computational Notebooks? by Ghahfarokhi, Mojtaba Mostafavi, Jahantigh, Hamed, Kianiangolafshani, Sepehr, Khademian, Ashkan, Asadi, Alireza, Heydarnoori, Abbas

“… on the impact of code metrics such as lines of code, API popularity, and complexity on this task…”
Get full text

OptiWISE: Combining Sampling and Instrumentation for Granular CPI Analysis by Guo, Yuxin, Chadwick, Alex W., Erdos, Marton, Bora, Utpal, Vougioukas, Ilias, Gabrielli, Giacomo, Jones, Timothy M.

“… Existing profiling tools typically either sample hardware performance counters or instrument the program with extra instructions to analyze its execution…”
Get full text

GPA: A GPU Performance Advisor Based on Instruction Sampling by Zhou, Keren, Meng, Xiaozhu, Sai, Ryuichi, Mellor-Crummey, John

“… Existing performance tools only provide coarse-grained tuning advice at the kernel level…”
Get full text

Performance Characterization of Popular DNN Models on Out-of-Order CPUs by Prieto, Pablo, Abad, Pablo, Gregorio, Jose Angel, Puente, Valentin

“… However, the ubiquity of DNN models is rapidly extending the presence of this software to general-purpose CPUs…”
Get full text

White-Box Performance-Influence Models: A Profiling and Learning Approach by Weber, Max, Apel, Sven, Siegmund, Norbert

“…Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at…”
Get full text

Navigating Mobile Testing Evaluation: A Comprehensive Statistical Analysis of Android GUI Testing Metrics by Lan, Yuanhong, Lu, Yifei, Pan, Minxue, Li, Xuandong

“… Code coverage and fault detection are two primary metrics for evaluating the effectiveness of these testing approaches…”
Get full text

Scaler: Efficient and Effective Cross Flow Analysis by Tang, Jiaxun, Xiang, Mingcan, Wang, Yang, Wu, Bo, Chen, Jianjun, Liu, Tongping

“…) of a complex system can interact with each other. However, few existing tools focus on understanding such interactions…”
Get full text

White-Box Performance-Influence Models: A Profiling and Learning Approach (Replication Package) by Weber, Max, Apel, Sven, Siegmund, Norbert

“… Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup…”
Get full text

Detection of Duplicate Defect Reports Using Natural Language Processing by Runeson, P., Alexandersson, M., Nyholm, O.

“… In order to identify duplicates, we investigate using natural language processing (NLP) techniques to support the identification…”
Get full text

Revamping Sampling-Based PGO with Context-Sensitivity and Pseudo-instrumentation by He, Wenlei, Yu, Hongtao, Wang, Lei, Oh, Taewook

“… Profile-guided optimization (PGO) is a promising technique to improve application performance…”
Get full text

Performance Analysis with Bayesian Inference by Couderc, Noric, Reichenbach, Christoph, Soderberg, Emma

“… However, for non-statisticians, picking the right statistical tool to answer a research question can be challenging…”
Get full text

Interpretable Software Maintenance and Support Effort Prediction Using Machine Learning by Haldar, Susmita, Capretz, Luiz Fernando

“…Software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality…”
Get full text

RJoules: An Energy Measurement Tool for R by Chattaraj, Rajrupa, Chimalakonda, Sridhar

“…With the exponential growth of data, the demand for effective data analysis tools has increased significantly…”
Get full text

Bus Factor Explorer by Klimov, Egor, Ahmed, Muhammad Umair, Sviridov, Nikolai, Derakhshanfar, Pouria, Tuzun, Eray, Kovalenko, Vladimir

“…Bus factor (BF) is a metric that tracks knowledge distribution in a project. It is the minimal number of engineers that have to leave for a project to stall…”
Get full text

Understanding Architectural Complexity, Maintenance Burden, and Developer Sentiment -A Large-Scale Study by Cai, Yuanfang, He, Lanting, Qian, Jun, Kochinski, Yony, Zhang, Nan, Jaspan, Ciera, Bianco, Antonio

“…Intuitively, the more complex a software system is, the harder it is to maintain. Statistically, it is not clear which complexity metrics correlate with maintenance effort…”
Get full text

MBAPIS: Multi-Level Behavior Analysis Guided Program Interval Selection for Microarchitecture Studies by Cui, Hongwei, Cui, Yujie, Zhan, Honglan, Liang, Shuhao, Liu, Xianhua, Yang, Chun, Cheng, Xu

“…Understanding program behavior is crucial in computer architecture research, but the growing size of benchmarks makes analyzing and simulating entire programs…”
Get full text

AIBench Scenario: Scenario-Distilling AI Benchmarking by Gao, Wanling, Tang, Fei, Zhan, Jianfeng, Wen, Xu, Wang, Lei, Cao, Zheng, Lan, Chuanxin, Luo, Chunjie, Liu, Xiaoli, Jiang, Zihan

“…Modern real-world application scenarios like Internet services consist of a diversity of AI and non-AI modules with huge code sizes and long and complicated…”
Get full text