Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Mutation testing realizes the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases, and to support experimentation. Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a “mini-handbook”-style roadmap for the application of mutation testing as experimental methodology.

/pdf/mutation-testing-advances-an-analysis-and-survey-2wtvd7tjwe.pdf

Mutation Testing Advances: An Analysis and Survey

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

/pdf/automated-discovery-of-process-models-from-event-logs-review-4xk0fkkv97.pdf

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Process mining is a growing and promising study area focused on understanding processes and to help capture the more significant findings during real execution rather than, those methods that, only observed idealized process model. The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. We also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review. In this article, an overview regarding process mining is presented, the main research topics are identified, followed by identification of the most applied process mining algorithms, and finally application domains among different business segments are reported on. It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements. In application domains, the segments with major case studies are healthcare followed by information and communication technology, manufacturing, education, finance, and logistics.

Process mining techniques and applications – A systematic mapping study

One major challenge in self-adaptive systems is to assure the required quality properties. Formal methods provide the means to rigorously specify and reason about the behaviors of self-adaptive systems, both at design time and runtime. To the best of our knowledge, no systematic study has been performed on the use of formal methods in self-adaptive systems. As a result, there is no clear view on what methods have been used to verify self-adaptive systems, and what support these methods offer to software developers. As such insight is important for researchers and engineers, we performed a systematic literature review covering 12 main software engineering venues and 4 journals, resulting in 75 papers used for data collection. The study shows that the attention for self-adaptive software systems is gradually increasing, but the number of studies that employ formal methods remains low. The main focus of formalization is on modeling and reasoning. Model checking and theorem proving have gained limited attention. The main concerns of interest in formalization of self-adaptation are efficiency/performance and reliability. Important adaptation concerns, such as security and scalability, are hardly considered. To verify the concerns of interest, a set of new properties are defined, such as interference freedom, responsiveness, mismatch, and loss-tolerance. A relevant part of the studies use formal methods at runtime, but the use is limited to modeling and analysis. Formal methods can be applied to other runtime activities of self-adaptation, and there is a need for light-weight tools to support runtime verification.

/pdf/a-survey-of-formal-methods-in-self-adaptive-systems-4fvozdq7zn.pdf

A survey of formal methods in self-adaptive systems

Pervasive computing environments typically change frequently in terms of available resources and their properties. Applications in pervasive computing use contexts to capture these changes and adapt their behaviors accordingly. However, contexts available to these applications may be abnormal or imprecise due to environmental noises. This may result in context inconsistencies, which imply that contexts conflict with each other. The inconsistencies may set such an application into a wrong state or lead the application to misadjust its behavior. It is thus desirable to detect and resolve the context inconsistencies in a timely way. One popular approach is to detect context inconsistencies when contexts breach certain consistency constraints. Existing constraint checking techniques recheck the entire expression of each affected consistency constraint upon context changes. When a changed context affects only a constraint's subexpression, rechecking the entire expression can adversely delay the detection of other context inconsistencies. This article proposes a rigorous approach to identifying the parts of previous checking results that are reusable without entire rechecking. We evaluated our work on the Cabot middleware through both simulation experiments and a case study. The experimental results reported that our approach achieved over a fifteenfold performance improvement on context inconsistency detection than conventional approaches.

Partial constraint checking for context consistency in pervasive computing

The large-scale dynamic cloud computing environment has raised great challenges for fault diagnosis in Web applications: First, fluctuating workloads cause traditional application models to change over time; second, modeling the behaviors of complex applications usually requires domain knowledge which is difficult to obtain; third, managing large-scale applications manually is impractical for operators. To address these issues, this paper proposes an automatic fault (F) diagnosis (D) framework for (4) Web applications in cloud (C) computing (FD4C). In this paper, we propose an online incremental clustering method to recognize access behavior patterns. We also use correlation analysis to model the correlations between the workloads and application performance/resource utilization metrics in a specific access behavior pattern. FD4C detects faults by discovering the abrupt changes of correlation coefficients with control charts. Then, FD4C identifies the fault-related metrics using a feature selection method. To evaluate our proposal, we inject typical faults into TPC-W benchmark and apply FD4C to diagnose the injected faults. The experimental results show that FD4C can effectively detect the typical faults and accurately locate the metrics related to the faults.

FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing

Context-awareness allows pervasive applications to adapt to changeable computing environments. Contexts, the pieces of information that capture the characteristics of environments, are often error-prone and inconsistent due to noises. Various strategies have been proposed to enable automatic context inconsistency resolution. They are formulated on different assumptions that may not hold in practice. This causes applications to be less context-aware to different extents. In this paper, we investigate such impacts and propose our new resolution strategy. We conducted experiments to compare our work with major existing strategies. The results showed that our strategy is both effective in resolving context inconsistencies and promising in its support of applications using contexts.

http://www.cs.cityu.edu.hk/~wkchan/papers/icdcs08-xu+cheung+chan+ye.pdf

Heuristics-Based Strategies for Resolving Context Inconsistencies in Pervasive Computing Applications

Process mining, especially process discovery, has been utilized to extract process models from event logs. One challenge faced by process discovery is to identify concurrency effectively. State-of-the-art approaches employ activity orders in traces to undertake process discovery and they require stringent completeness notions of event logs. Thus, they may fail to extract appropriate processes when event logs cannot meet the completeness criteria. To address this problem, we propose in this paper a novel technique which leverages activity dependences in traces. Based on the observation that activities with no dependencies can be executed in parallel, our technique is in a position to discover processes with concurrencies even if the logs fail to meet the completeness criteria. That is, our technique calls for a weaker notion of completeness. We evaluate our technique through experiments on both real-world and synthetic event logs, and the conformance checking results demonstrate the effectiveness of our technique and its relative advantages compared with state-of-the-art approaches.

Process Discovery from Dependence-Complete Event Logs

Atomicity is a highly desirable property for achieving application consistency in service compositions. To achieve atomicity, a service composition should satisfy the atomicity sphere, a structural criterion for the backend processes of involved services. Existing analysis techniques for atomicity sphere generally assume complete knowledge of all involved backend processes. Such an assumption is invalid when some service providers do not release all details of their backend processes to service consumers outside the organizations. To address this problem, we propose a process algebraic framework to publish atomicity-equivalent public views from the backend processes. These public views extract relevant task properties and reveal only partial process details that service providers need to expose. Our framework enables the analysis of atomicity sphere for service compositions using these public views instead of their backend processes. This allows service consumers to choose suitable services such that their composition satisfies the atomicity sphere without disclosing the details of their backend processes. Based on the theoretical result, we present algorithms to construct atomicity-equivalent public views and to analyze the atomicity sphere for a service composition. Two case studies from supply chain and insurance domains are given to evaluate our proposal and demonstrate the applicability of our approach.

Chunyang Ye

Papers

Partial constraint checking for context consistency in pervasive computing

FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing

Heuristics-Based Strategies for Resolving Context Inconsistencies in Pervasive Computing Applications

Process Discovery from Dependence-Complete Event Logs

Atomicity Analysis of Service Composition across Organizations