Economic Control of Quality of Manufactured Product.

Control of Discrete-event Systems provides a survey of the most important topics in the discrete-event systems theory with particular focus on finite-state automata, Petri nets and max-plus algebra. Coverage ranges from introductory material on the basic notions and definitions of discrete-event systems to more recent results. Special attention is given to results on supervisory control, state estimation and fault diagnosis of both centralized and distributed/decentralized systems developed in the framework of the Distributed Supervisory Control of Large Plants (DISC) project. Later parts of the text are devoted to the study of congested systems though fluidization, an over approximation allowing a much more efficient study of observation and control problems of timed Petri nets. Finally, the max-plus algebraic approach to the analysis and control of choice-free systems is also considered. Control of Discrete-event Systems provides an introduction to discrete-event systems for readers that are not familiar with this class of systems, but also provides an introduction to research problems and open issues of current interest to readers already familiar with them. Most of the material in this book has been presented during a Ph.D. school held in Cagliari, Italy, in June 2011. This book constitutes the refereed proceedings of the Third International Workshop on Tools and Algorithms for the Construction and Analysis of Systems, TACAS '97, held in Enschede, The Netherlands, in April 1997. The book presents 20 revised full papers and 5 tool demonstrations carefully selected out of 54 submissions; also included are two extended abstracts and a full paper corresponding to invited talks. The papers are organized in topical sections on space reduction techniques, tool demonstrations, logical techniques, verification support, specification and analysis, and theorem proving, model checking and applications. The refereed proceedings of the 24th International Conference on Applications and Theory of Petri Nets, ICATPN 2003, held in Eindhoven, The Netherlands, in June 2003. The 25 revised full papers presented together with 6 invited contributions were carefully reviewed and selected from 77 submissions. All current issues on research and development in the area of Petri nets are addressed, in particular concurrent systems design and analysis, model checking, networking, business process modeling, formal methods in software engineering, agent systems, systems specification, systems validation, discrete event systems, protocols, and prototyping. The contents of this volume are application oriented. The volume contains a de tailed presentation of 19 applications of CP-nets, covering a broad range of ap plication areas. Most of the projects have been carried out in an industrial set ting.

Coloured Petri Nets Basic Concepts, Analysis Methods and Practical Use

Researchers in software engineering have attempted to improve software development by mining and analyzing software repositories. Since the majority of the software engineering data is unstructured, researchers have applied Information Retrieval (IR) techniques to help software development. The recent advances of IR, especially statistical topic models, have helped make sense of unstructured data in software repositories even more. However, even though there are hundreds of studies on applying topic models to software repositories, there is no study that shows how the models are used in the software engineering research community, and which software engineering tasks are being supported through topic models. Moreover, since the performance of these topic models is directly related to the model parameters and usage, knowing how researchers use the topic models may also help future studies make optimal use of such models. Thus, we surveyed 167 articles from the software engineering literature that make use of topic models. We find that i) most studies centre around a limited number of software engineering tasks; ii) most studies use only basic topic models; iii) and researchers usually treat topic models as black boxes without fully exploring their underlying assumptions and parameter values. Our paper provides a starting point for new researchers who are interested in using topic models, and may help new researchers and practitioners determine how to best apply topic models to a particular software engineering task.

A survey on the use of topic models when mining software repositories

https://zenodo.org/record/1997449/files/SS18.pdf

A survey on software smells

Green Mining is a field of MSR that studies software energy consumption and relies on software performance data. Unfortunately there is a severe lack of publicly available software power use performance data. This means that green mining researchers must generate this data themselves by writing tests, building multiple revisions of a product, and then running these tests multiple times (10+) for each software revision while measuring power use. Then, they must aggregate these measurements to estimate the energy consumed by the tests for each software revision. This is time consuming and is made more difficult by the constraints of mobile devices and their OSes. In this paper we propose, implement, and demonstrate Green Miner: the first dedicated hardware mining software repositories testbed. The Green Miner physically measures the energy consumption of mobile devices (Android phones) and automates the testing of applications, and the reporting of measurements back to developers and researchers. The Green Miner has already produced valuable results for commercial Android application developers, and has been shown to replicate other power studies' results.

GreenMiner: a hardware based mining software repositories software energy consumption framework

Object-Relational Mapping (ORM) provides developers a conceptual abstraction for mapping the application code to the underlying databases. ORM is widely used in industry due to its convenience; permitting developers to focus on developing the business logic without worrying too much about the database access details. However, developers often write ORM code without considering the impact of such code on database performance, leading to cause transactions with timeouts or hangs in large-scale systems. Unfortunately, there is little support to help developers automatically detect suboptimal database accesses. In this paper, we propose an automated framework to detect ORM performance anti-patterns. Our framework automatically flags performance anti-patterns in the source code. Furthermore, as there could be hundreds or even thousands of instances of anti-patterns, our framework provides sup- port to prioritize performance bug fixes based on a statistically rigorous performance assessment. We have successfully evaluated our framework on two open source and one large-scale industrial systems. Our case studies show that our framework can detect new and known real-world performance bugs and that fixing the detected performance anti- patterns can improve the system response time by up to 98%.

/pdf/detecting-performance-anti-patterns-for-applications-11n58o8vkt.pdf

Detecting performance anti-patterns for applications developed using object-relational mapping

The goal of performance regression testing is to check for performance regressions in a new version of a software system. Performance regression testing is an important phase in the software development process. Performance regression testing is very time consuming yet there is usually little time assigned for it. A typical test run would output thousands of performance counters. Testers usually have to manually inspect these counters to identify performance regressions. In this paper, we propose an approach to analyze performance counters across test runs using a statistical process control technique called control charts. We evaluate our approach using historical data of a large software team as well as an open-source software project. The results show that our approach can accurately identify performance regressions in both software systems. Feedback from practitioners is very promising due to the simplicity and ease of explanation of the results.

/pdf/automated-detection-of-performance-regressions-using-1wj3noxotc.pdf

Automated detection of performance regressions using statistical process control techniques

To help improve the performance of database-centric cloud-based web applications, developers usually use caching frameworks to speed up database accesses. Such caching frameworks require extensive knowledge of the application to operate effectively. However, all too often developers have limited knowledge about the intricate details of their own application. Hence, most developers find configuring caching frameworks a challenging and time-consuming task that requires extensive and scattered code changes. Furthermore, developers may also need to frequently change such configurations to accommodate the ever changing workload. In this paper, we propose CacheOptimizer, a lightweight approach that helps developers optimize the configuration of caching frameworks for web applications that are implemented using Hibernate. CacheOptimizer leverages readily-available web logs to create mappings between a workload and database accesses. Given the mappings, CacheOptimizer discovers the optimal cache configuration using coloured Petri nets, and automatically adds the appropriate cache configurations to the application. We evaluate CacheOptimizer on three open-source web applications. We find that i) CacheOptimizer improves the throughput by 27--138%; and ii) after considering both the memory cost and throughput improvement, CacheOptimizer still brings statistically significant gains (with mostly large effect sizes) in comparison to the application's default cache configuration and to blindly enabling all possible caches.

CacheOptimizer: helping developers configure caching frameworks for hibernate-based database-centric web applications

SUMMARY

Substantial research in software engineering focuses on understanding the dynamic nature of software systems in order to improve software maintenance and program comprehension. This research typically makes use of automated instrumentation and profiling techniques after the fact, that is, without considering domain knowledge. In this paper, we examine another source of dynamic information that is generated from statements that have been inserted into the code base during development to draw the system administrators' attention to important run-time phenomena. We call this source communicated information (CI). Examples of CI include execution logs and system events. The availability of CI has sparked the development of an ecosystem of Log Processing Apps (LPAs) that surround the software system under analysis to monitor and document various run-time constraints. The dependence of LPAs on the timeliness, accuracy and granularity of the CI means that it is important to understand the nature of CI and how it evolves over time, both qualitatively and quantitatively. Yet, to our knowledge, little empirical analysis has been performed on CI and its evolution. In a case study on two large open source and one industrial software systems, we explore the evolution of CI by mining the execution logs of these systems and the logging statements in the source code. Our study illustrates the need for better traceability between CI and the LPAs that analyze the CI. In particular, we find that the CI changes at a high rate across versions, which could lead to fragile LPAs. We found that up to 70% of these changes could have been avoided and the impact of 15% to 80% of the changes can be controlled through the use of robust analysis techniques by LPAs. We also found that LPAs that track implementation-level CI (e.g. performance analysis) and the LPAs that monitor error messages (system health monitoring) are more fragile than LPAs that track domain-level CI (e.g. workload modelling), because the latter CI tends to be long-lived. Copyright © 2013 John Wiley & Sons, Ltd.

/pdf/an-exploratory-study-of-the-evolution-of-communicated-80cg25buic.pdf

An exploratory study of the evolution of communicated information about the execution of large software systems

Performance testing is conducted before deploying system updates in order to ensure that the performance of large software systems did not degrade (i.e., no performance regressions). During such testing, thousands of performance counters are collected. However, comparing thousands of performance counters across versions of a software system is very time consuming and error-prone. In an effort to automate such analysis, model-based performance regression detection approaches build a limited number (i.e., one or two) of models for a limited number of target performance counters (e.g., CPU or memory) and leverage the models to detect performance regressions. Such model-based approaches still have their limitations since selecting the target performance counters is often based on experience or gut feeling. In this paper, we propose an automated approach to detect performance regressions by analyzing all collected counters instead of focusing on a limited number of target counters. We first group performance counters into clusters to determine the number of performance counters needed to truly represent the performance of a system. We then perform statistical tests to select the target performance counters, for which we build regression models. We apply the regression models on new version of the system to detect performance regressions.We perform two case studies on two large systems: one open-source system and one enterprise system. The results of our case studies show that our approach can group a large number of performance counters into a small number of clusters. Our approach can successfully detect both injected and real-life performance regressions in the case studies. In addition, our case studies show that our approach outperforms traditional approaches for analyzing performance counters. Our approach has been adopted in industrial settings to detect performance regressions on a daily basis.

/pdf/automated-detection-of-performance-regressions-using-n4y96pyrm1.pdf

Mohamed Nasser

Papers

Detecting performance anti-patterns for applications developed using object-relational mapping

Automated detection of performance regressions using statistical process control techniques

CacheOptimizer: helping developers configure caching frameworks for hibernate-based database-centric web applications

An exploratory study of the evolution of communicated information about the execution of large software systems

Automated Detection of Performance Regressions Using Regression Models on Clustered Performance Counters