End-to-end test cases that exercise the application under test via its user interface (UI) are known to be hard for developers to read and understand; consequently, diagnosing failures in these tests and maintaining them can be tedious. Techniques for computing natural-language descriptions of test cases can help increase test readability. However, so far, such techniques have been developed for unit test cases; they are not applicable to end-to-end test cases.

With the adoption of cloud services and the reliability and resiliency it offers, enterprises are eager to understand how many of their legacy applications can be containerized. We propose Application Containerization advisor (ACA), a framework that provides a containerization advisory for legacy applications. Given an application description in terms of its technical components, ACA proposes a multi-step process that standardizes the raw inputs and curates technology stack into various components, detects missing components and finally recommends the best possible containerization approach.

To support IT change automation in cloud-native deployments, we propose to discover change actions from IT change requests. Traditional approaches to discover actions rely on a pre-established set of actions. However in practice, the catalog dictionaries rarely cover a sufficient portion of the IT change requests, resulting in missed automation opportunities. To this end, supervised and semi-supervised approaches have been proposed to detect change actions, but fall short in covering new action discovery as the IT environment evolves. We propose an ensemble technique of parametric and non-parametric grammar-based methods to discover change actions from IT change requests. We evaluate our approach on an IT dataset and find our approach provides significant coverage of actions compared to baseline approaches.

When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller microservices. While many tools have been proposed for this task, we warn that the evaluation of those approaches has been incomplete; e.g. minimal prior exploration of hyperparameter optimization. Using a set of open source Java EE applications, we show here that (a) such optimization can significantly improve microservice partitioning; and that (b) an open issue for future work is how to find which optimizer works best for different problems. To facilitate that future work, see https://github.com/yrahul3910/ase-tuned-mono2micro for a reproduction package for this research.

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results.

Commitments and requests are a hallmark of collaborative communication, especially in organizational settings.Identifying specific tasks being committed to or requests from emails and chat messages can enable important downstream tasks, such as producing to-do lists, reminders, and calendar entries.State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks.Accordingly, we propose Liṅ, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet.Our evaluations show that Liṅ yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.

Mono2Micro is an AI-based toolchain that provides recommendations for decomposing legacy web applications into microservice partitions. Mono2Micro consists of a set of tools that collect static and runtime information from a monolithic application and process the information using an AI-based technique to generate recommendations for partitioning the application classes. Each partition represents a candidate microservice or a grouping of classes with similar business functionalities. Mono2Micro takes a temporo-spatial clustering approach to compute meaningful and explainable partitions. It generates two types of partition recommendations. First, it computes business-logic-seams-based partitions that represent a desired encapsulation of business functionalities. However, such a recommendation may cut across data dependencies between classes, accommodating which could require significant application updates. To address this, Mono2Micro computes natural-seams-based partitions, which respect data dependencies. We describe the set of tools that comprise Mono2Micro and illustrate them using a well-known open-source JEE application.

We study how emotions influence norm outcomes in decision-making contexts. Following the literature, we provide baseline Dynamic Bayesian models to capture an agent's two perspectives on a directed norm. Unlike the literature, these models are holistic in that they incorporate not only norm outcomes and emotions but also trust and goals. We obtain data from an empirical study involving game play with respect to the above variables. We provide a step-wise process to discover two new Dynamic Bayesian models based on maximizing log-likelihood scores with respect to the data. We compare the new models with the baseline models to discover new insights into the relevant relationships. Our empirically supported models are thus holistic and characterize how emotions influence norm outcomes better than previous approaches.

Existing approaches to recommend services using natural language queries are supervised or unsupervised. Supervised approaches rely on a dataset with natural language queries annotated with categorizing labels. As the annotation process is manual and requires deep domain knowledge, these approaches are not readily applicable on new datasets. On the other hand, unsupervised approaches overcome the limitation. To date, unsupervised approaches are primarily based on matching keywords, entity relationships, topics and clusters. Keywords and entity relationships ignore the semantic similarity between a query and services. Topics and clusters capture the semantic similarity, but rely on mashups that explicitly capture relationships between services. Again, for new services, the information are not readily available. We propose NL2API, a framework that relies solely on service descriptions for recommending services. NL2API has the benefit of being immediately applicable as a bootstrap recommender for new datasets. To capture relationships among services, NL2API provides different approaches to construct communities where a community represents an abstraction over a group of services. Based on the communities and users' queries, NL2API applies a query matching approach to recommend top-k services. We evaluate NL2API on datasets collected from Programmable Web and API Harmony. Our evaluation shows that for sizable datasets such as Programmable Web NL2API outperforms baseline approaches.

Existing approaches to compute team process measures are primarily based on survey ratings, semantic classification of communications, and social network analyses. Although existing approaches reveal important information about team performance, they face specific limitations. Survey methodologies are in general unreliable, biased, and not dynamic; communication classifications are often a-theoretical; and social network analytics ignore the meanings of messages. Accordingly, we develop a better-defined formal empirical approach for computing team process measures. Our contribution builds on existing work in semantic classification of messages in broadcast communications and proposes a general set of meanings of messages for team processes. Using the meanings of messages, we propose formal approaches to compute team process measures. We evaluate these measures using a military data set and find the following. First, our text mining approach to infer meanings of messages significantly improves over the bag of words approach and yields macroaverage and microaverage F-measures of 70% and 80%, respectively. Second, compared with baseline measures such as degree centrality, cognitive processes remain significantly stable with time, whereas measures such as affective process significantly increase with time.

In QoS-aware service selection, a service requester seeks to maximize its utility by selecting a service provider that charges the lowest service price while meeting the requester's QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and candidate service providers have a conflicting interest over service prices. Since a provider would not reveal its minimum acceptabl price, it is important for a requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experience. The experimental results show our approach can find the optimal service providers efficiently and effectively.

A stakeholder's beliefs influence his or her goals. However, a stakeholder's beliefs may not be consistent with the goals of all stakeholders of a system being constructed. Such belief-goal inconsistencies could manifest themselves as conflicting goals of the system to be. We propose Arg-ACH, a novel approach for capturing inconsistencies between stakeholders' goals and beliefs, and resolving goal conflicts. Arg-ACH employs a hybrid of (1) the analysis of competing hypotheses (ACH), a structured analytic technique, for systematically eliciting stakeholders' goals and beliefs, and (2) rational argumentation for determining belief-goal inconsistencies to resolve conflicts. Arg-ACH treats conflicting goals as hypotheses that compete with each other and the winning hypothesis as a goal of the system to be. Arg-ACH systematically captures the trail of a requirements engineer's thought process in resolving conflicts. We evaluated Arg-ACH via a study in which 20 subjects applied Arg-ACH or ACH to resolve goal conflicts in a sociotechnical system concerning national security. We found that Arg-ACH is superior to ACH with respect to completeness and coverage of belief search; length of belief chaining; ease of use; explicitness of the assumptions made; and repeatability of conclusions across subjects. Not surprisingly, Arg-ACH required more time than ACH: although this is justified by improvements in quality, the gap could be reduced through better tooling.

We provide an approach to estimate trust between agents from their interactions. Our approach takes a probabilistic model of trust founded on commitments. We assume commitments to estimate trust because a commitment describes what an agent may expect of another. Therefore, the satisfaction or violation of a commitment provides a natural basis for determining how much to trust another agent. We evaluate our approach empirically. In one study, 30 subjects read emails extracted from the Enron dataset augmented with some synthetic emails to capture commitment operations missing in the Enron corpus. The subjects estimated trust between each pair of communicating participants. We trained model parameters for each subject with respect to our automated analysis of the emails, showing that our trained parameters yield a lower prediction error of a subject's trust rating given automatically inferred commitments than fixed parameters.

How an agent trusts another naturally depends on the outcomes of their interactions. Previous approaches have treated the outcomes in a domain-specific way. We propose an approach relating trust to the domain-independent notion of commitments. We conduct an empirical study to evaluate our approach, in which subjects read emails extracted from the Enron dataset (augmented with some synthetic emails for completeness), and estimate trust between each pair of communicating participants. We propose a probabilistic model for trust based on commitment outcomes and show how to train its parameters for each subject based on the subject's trust assessments. The results are promising, though imperfect. Our main contribution is to launch a research program into computing trust based on a semantically well-founded account of agent interactions.

Service selection, where some of the services are accessed indirectly as constituents of composite services, is difficult for the following reasons: (1) the interpretation of service qualities is subjective; (2) evidence must be combined from multiple sources; (3) service profiles change dynamically; and (4) constituent services may be only partially observable behind composite services. We propose an approach where we map service qualities to a common probabilistic trust metric. Whereas current trust approaches estimate the trustworthiness of a composite service based on a fully observable and static setting, we propose a statistical approach built on expectation maximized over a finite mixture model. Our experiments show that our approach can dynamically punish or reward the constituents of composite services while making only partial observations.