No abstract

Field monitoring techniques can collect data about the behavior of software applications while running in the field, with real users and real data. Developers can exploit the information extracted from the field to timely improve, tune, and fix their systems, anticipating feedback by users. It is, however, challenging to extract a relevant amount of information from field executions without introducing significant overhead. This paper addresses this challenge by studying how to inexpensively trace data in-memory while postponing save operations to idle time, so that the operations requested by users are exposed to a negligible overhead only. In particular, this paper presents delayed saving, a technique that efficiently traces references to objects, opportunistically saving information only when the monitored application is not serving any user request. Storing references and postponing save operations may introduce inaccuracy in the collected data, that is, a lately saved object might be in a different state compared to the state of the object at the time it was traced. Our evaluation shows that the level of inaccuracy introduced by delayed saving is limited compared to its efficiency and low intrusiveness. INDEX TERMS Monitoring, tracing, dynamic analysis.

Cyber-physical systems (CPSs) typically consist of a wide set of integrated, heterogeneous components; consequently, most of their critical failures relate to the interoperability of such components. Unfortunately, most CPS test automation techniques are preliminary and industry still heavily relies on manual testing. With potentially incomplete, manually-generated test suites, it is of paramount importance to assess their quality. Though mutation analysis has demonstrated to be an effective means to assess test suite quality in some specific contexts, we lack approaches for CPSs. Indeed, existing approaches do not target interoperability problems and cannot be executed in the presence of black-box or simulated components, a typical situation with CPSs. In this paper, we introduce data-driven mutation analysis, an approach that consists in assessing test suite quality by verifying if it detects interoperability faults simulated by mutating the data exchanged by software components. To this end, we describe a data-driven mutation analysis technique (DaMAT ) that automatically alters the data exchanged through data buffers. Our technique is driven by fault models in tabular form where engineers specify how to mutate data items by selecting and configuring a set of mutation operators. We have evaluated DaMAT with CPSs in the space domain; specifically, the test suites for the software systems of a microsatellite and nanosatellites launched on orbit last year. Our results show that the approach effectively detects test suite shortcomings, is not affected by equivalent and redundant mutants, and entails acceptable costs.

Field data is an invaluable source of information for testers and developers because it witnesses how software systems operate in real environments, capturing scenarios and configurations relevant to end-users. Unfortunately, collecting traces might be resource-consuming and can significantly affect the user experience, for instance causing annoying slowdowns.Existing monitoring techniques can control the overhead introduced in the applications by reducing the amount of collected data, for instance by collecting each event only with a given probability. However, collecting fewer events limits the amount of information extracted from the field and may fail in providing a comprehensive picture of the behavior of a program.In this paper we present fragmented monitoring, a monitoring technique that addresses the issue of collecting information from the field without annoying users. The key idea of fragmented monitoring is to reduce the overhead by recording partial traces (fragments) instead of full traces, while annotating the beginning and the end of each fragment with state information. These annotations are exploited offline to derive traces that might be likely observed in the field and that could not be collected directly due to the overhead that would be introduced in a program.

Fully assessing the robustness of a software application in-house is infeasible, especially considering the huge variety of hardly predictable stimuli, environments, and configurations that applications must handle in the field. For this reason, modern testing and analysis techniques can often process data extracted from the field, such as crash reports and profile data, or can even be executed directly in the field, for instance to diagnose and correct problems. In all these cases, collection, processing, and distribution of field data must be done seamlessly and unobstrusively while users interact with their applications.To limit the intrusiveness of in-the-field monitoring a common approach is to reduce the amount of collected data (e.g., to rare events and to crash dumps), which, however, may severely affect the effectiveness of the techniques that exploit field data.The objective of this Ph.D. thesis is to define solutions for collecting field data in a cost effective way without affecting the quality of the user experience. This result can enable a new range of testing and analysis solutions that extensively exploit field data.