Continuity and steadiness are vital for services with massive users, which requires the anomalies of services should be detected and resolved in a timely manner. Our previous work proposed a tool, namelyImpAPTr (Impact Analysis based on Pruning Tree), to identify the combination of multiple dimensional attributes as the clues leading to the root cause of service anomalies. However,ImpAPTrapplies a threshold driven strategy, i.e., it needs to be triggered by a$\geq 0.05\%$drop of the success rate of the service calls (abbr.SRSC), which may face problems in an atypical yet pervasive situation in field application. For example, the combination of trivial anomalies (i.e., each causes a drop less than 0.05% toSRSC) can lead to a far more than 0.05% drop onSRSC. Besides, a suitable threshold is usually hard to be determined, etc. To address these problems, we propose a new method, namelyImpAPTr+in this paper to free the constraint of the 0.05% threshold. The basic idea is to involve time dimension and identify clues across multiple time intervals of data. We performed evaluation on three typical methods (i.e.,ImpAPTr+,R-AdtributorandSqueeze) with both production environment dataset and simulation dataset. The former dataset is directly retrieved from the service monitoring data inMeituan, one of the largest on-line service providers worldwide. The latter dataset is fabricated also using the monitoring data from the same company. The results indicate: (1)ImpAPTr+outperforms previous approaches to a large degree in terms of accuracy. (2) BothImpAPTr+andR-Adtributorare able to find proper clues within seconds. (3)ImpAPTr+tends to find proper clues with shorter time intervals (i.e., less data), which implies that the method is more suitable for near real-time monitoring scenarios.

Background: Logs provide crucial information to understand the dynamic behavior of software systems in modern software development and maintenance. Usually, logs are produced by log statements which will be triggered and executed under certain conditions. However, current studies paid very limited attention to developers' Intentions and Concerns (I&C) on logging practice, leading uncertainty that whether the developers' I&C are properly reflected by log statements and questionable capability to capture the expected information of system behaviors in logs. Objective: This study aims to reveal the status of developers' I&C on logging practice and more importantly, how the I&C are properly reflected in software source code in real-world software development. Method: We collected evidence from two sources of a series of interviews and source code analysis which are conducted in a big-data company, followed by consolidation and analysis of the evidence. Results: Major gaps and inconsistencies have been identified between the developers' I&C and real log statements in source code. Many code snippets contained no log statements that the interviewees claimed to have inserted. Conclusion: Developers' original I&C towards logging practice are usually poorly realized, which inevitably impacted the motivation and purpose to conduct this practice.

For many on-line systems with massive users, to provide services continuously and steadily is vital for business, which requires the anomalies of services should be located and resolved in a timely manner. As a common IT infrastructure, various APM (Application Performance Management) systems/frameworks have been adopted to monitor each call request to a service. Nevertheless, the call request may contain multidimensional attributes (e.g., City, ISP, Platform, etc.), which may further contain multiple values (e.g., ISP could be T-Mobile, CMCC, etc.). As a result, an anomaly such as DSR (Declining Success Rate) to service typically occurs with a combination of such attribute values, which creates major challenges to locate the root cause of the anomaly due to potentially huge numbers of the combinations. In this paper, we propose a novel method, ImpAPTr (Impact Analysis based on Pruning Tree), to identify the combination of dimensional attributes as the clues leading to the root cause of anomalies regarding DSR timely. In the evaluation with the simulated dataset, ImpAPTr detects valid clues in milliseconds with an accuracy of 99.37% (within the top 10 candidate results), 97.72% (top 5), and 94.51% (top 3), respectively, which outperforms previous approaches to a large degree. A field test with a production environment dataset indicates that ImpAPTr is able to detect valid clues in a few seconds.

As a common IT infrastructure, APM (Application Performance Management) systems have been widely adopted to monitor call requests to an on-line service. Usually, each request may contain multi-dimensional attributes (e.g., City, ISP, Platform, etc.), which may become the reason for a certain anomaly regarding DSR (Declining Success Rate) of service calls either solely or as a combination. Moreover, each attribute may also have multiple values (e.g., ISP could be T-Mobile, Vodafone, CMCC, etc.), rendering intricate root causes and huge challenges to identify the root causes. In this paper, we propose a prototype tool, ImpAPTr (Impact Analysis based on Pruning Tree), to identify the combination of dimensional attributes as the clues to dig out the root causes of anomalies regarding DSR of a service call in a timely manner. ImpAPTr has been evaluated in MeiTuan, one of the biggest on-line service providers. Performance regarding the accuracy outperforms several previous tools in the same field.