JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability

Property-aware program sampling

Monitoring or profiling programs provides us with an understanding for its further improvement and analysis. Typically, for monitoring or profiling, the program is instrumented to execute additional code that collects necessary data. However, a widely-understood problem with this approach is that program instrumentation can result in significant execution overhead. A number of techniques based on statistical sampling have been proposed to reduce this overhead. Statistical sampling based instrumentation techniques, although effective in reducing the overall overhead, often lead to poor coverage or incomplete results. The contribution of this work is a profiling technique that we call property-aware program sampling. Our sampling technique uses program slicing to reduce the scope of instrumentation and slice fragmentsto decompose large program slices into more manageable, logically related parts for instrumentation, thereby improving the scalability of monitoring and profiling techniques. The technical underpinnings of our work include the notion of slice fragments and an efficient technique for computing a reduced set of slice fragments

Technical Reports (2004 - 2009)

Authors of Technical Reports (2005-2009): Choueiry, Berthe Cohen, Myra Deogun, Jitender Dwyer, Matthew Elbaum, Sebastian Goddard, Steve Henninger, Scott Jiang, Hong Lu, Ying Ramamurthy, Byrav Rothermel, Gregg Scott, Stephen Seth, Sharad Soh, Leen-Kiat Srisa-an, Witty Swanson, David Variyam, Vinodchandran Wang, Jun Xu, Lison

Practical API Protocol Checking with Access Permissions

Reusable APIs often define usage protocols. We previously developed a sound modular type system that checks compliance with typestate-based protocols while affording a great deal of aliasing flexibility. We also developed Plural, a prototype tool that embodies our approach as an automated static analysis and includes several extensions we found useful in practice. This paper evaluates our approach along the following dimensions: (1) We report on experience in specifying relevant usage rules for a large Java standard API with our approach. We also specify several other Java APIs and identify recurring patterns. (2) We summarize two case studies in verifying third-party open-source code bases with few false positives using our tool. We discuss how tool shortcomings can be addressed either with code refactorings or extensions to the tool itself. These results indicate that our approach can be used to specify and enforce real API protocols in practice

[技術・研究報告] Eclipse上で動作する効率の良いオンラインおよびオフライン動的解析プラットフォーム

ソフトウェア工学のさまざまな分野において動的解析技術が用いられている．動的解析とは，プログラムの実行時の情報を収集し解析を行うプログラム解析技術で，解析対象となるプログラムの実行終了後に解析を行うオフライン解析と，実行の途中でそれまでに収集された情報の解析を行うオンライン解析に分類することができる．本論文では，オンラインおよびオフライン解析ツールの開発を支援するため，Eclipse上で動作する動的解析プラットフォームを開発したので報告する．具体的には，これまで，特にオンライン解析においてツール毎に個別に実装されてきた実行時情報の収集処理を共通化し，さらに収集された実行時情報を解析するための共通基盤をEclipse上で提供することによって開発の効率化を図る．本プラットフォームが提供する収集処理は，幅広い動的解析手法で利用できるよう詳細な実行時情報を効率よく収集できるという特長を持つ．またオンライン解析の共通基盤としては，解析アルゴリズムをオンライン化することによって生じるオーバーヘッドをできる限り抑えるよう設計上の工夫を行っている．本論文ではその設計を採用した場合と採用しない場合の解析速度の比較を行い，設計の妥当性について評価を行ったので報告する

Adaptive monitoring: A systematic mapping

Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems’ customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches. Objective: To characterize the current state of the art on adaptive monitoring, specifically to: (a) identify the main concepts in the adaptive monitoring topic; (b) determine the demographic characteristics of the studies published in this topic; (c) identify how adaptive monitoring is conducted and evaluated by the different approaches; (d) identify patterns in the approaches supporting adaptive monitoring. Method: We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions. Results: We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies’ demographic data and arranged the studies into approaches; (3) characterized the main approaches’ contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.Peer ReviewedPostprint (author's final draft

Runtime Verification with Controllable Time Predictability and Memory Utilization

The goal of runtime verifi cation is to inspect the well-being of a system by employing a monitor during its execution. Such monitoring imposes cost in terms of resource utilization. Memory usage and predictability of monitor invocations are the key indicators of the quality of a monitoring solution, especially in the context of embedded systems. In this work, we propose a novel control-theoretic approach for coordinating time predictability and memory utilization in runtime monitoring of real-time embedded systems. In particular, we design a PID controller and four fuzzy controllers with di erent optimization control objectives. Our approach controls the frequency of monitor invocations by incorporating a bounded memory bu er that stores events which need to be monitored. The controllers attempt to improve time predictability, and maximize memory utilization, while ensuring the soundness of the monitor. Unlike existing approaches based on static analysis, our approach is scalable and well-suited for reactive systems that are required to react to stimuli from the environment in a timely fashion. Our experiments using two case studies (a laser beam stabilizer for aircraft tracking, and a Bluetooth mobile payment system) demonstrate the advantages of using controllers to achieve low variation in the frequency of monitor invocations, while maintaining maximum memory utilization in highly non-linear environments. In addition to this problem, the thesis presents a brief overview of our preceding work on runtime verifi cation