On The Impact of Passive Voice Requirements on Domain Modelling

Context: The requirements specification is a central arte- fact in the software engineering (SE) process, and its quality (might) influence downstream activities like implementation or testing. One quality defect that is often mentioned in standards is the use of passive voice. However, the con- sequences of this defect are still unclear. Goal: We need to understand whether the use of passive voice in requirements has an influence on other activities in SE. In this work we focus on domain modelling. Method: We designed an experiment, in which we ask students to draw a domain model from a given set of requirements written in active or passive voice. We compared the completeness of the resulting domain model by counting the number of missing actors, domain objects and their associations with respect to a specified solution. Results: While we could not see a difference in the number of missing actors and objects, participants which received passive sentences missed almost twice the associations. Conclusion: Our experiment indicates that, against common knowledge, actors and objects in a requirement can often be understood from the context. However, the study also shows that passive sentences complicate understanding how certain domain concepts are interconnected

In Quest for Requirements Engineering Oracles: Dependent Variables and Measurements for (good) RE

Context: For many years, researchers and practitioners have been proposing various methods and approaches to Requirements Engineering (RE). Those contributions remain, however, too often on the level of apodictic discussions with- out having proper knowledge about the practical problems they propagate to address, or how to measure the success of the contributions when applying them in practical con- texts. While the scientific impact of research might not be threatened, the practical impact of the contributions is. Aim: We aim at better understanding practically relevant variables in RE, how those variables relate to each other, and to what extent we can measure those variables. This allows for the establishment of generalisable improvement goals, and the measurement of success of solution proposals. Method: We establish a first empirical basis of de- pendent variables in RE and means for their measurement. We classify the variables according to their dimension (e.g. RE, company, SW project), their measurability, and their actionability. Results: We reveal 93 variables with 167 dependencies of which a large subset is measurable directly in RE while further variables remain unmeasurable or have too complex dependencies for reliable measurements. We critically reflect on the results and show direct implications for research in the field of RE. Conclusion: We discuss a variety of conclusions we can draw from our results. For example, we show a set of first improvement goals directly usable for evidence-based RE research such as "increase flexibility in the RE process", we discuss suitable study types, and, finally, we can underpin the importance of replication studies to obtain generalisability

On the impact of passive voice requirements on domain modelling

Context: The requirements specification is a central arte- fact in the software engineering (SE) process, and its quality (might) influence downstream activities like implementation or testing. One quality defect that is often mentioned in standards is the use of passive voice. However, the con- sequences of this defect are still unclear. Goal: We need to understand whether the use of passive voice in requirements has an influence on other activities in SE. In this work we focus on domain modelling. Method: We designed an experiment, in which we ask students to draw a domain model from a given set of requirements written in active or passive voice. We compared the completeness of the resulting domain model by counting the number of missing actors, domain objects and their associations with respect to a specified solution. Results: While we could not see a difference in the number of missing actors and objects, participants which received passive sentences missed almost twice the associations. Conclusion: Our experiment indicates that, against common knowledge, actors and objects in a requirement can often be understood from the context. However, the study also shows that passive sentences complicate understanding how certain domain concepts are interconnected

Detecting inconsistencies in wrappers: A case study

Exchangeability between software components such as operating systems, middleware, databases, and hardware components is a common requirement in many software systems. One way to enable exchangeability is to promote indirect use through a common interface and an implementation for each component that wraps the original component. As developers use the interface instead of the underlying component, they assume that the software system will behave in a specific way independently of the actual component in use. However, differences in the implementations of the wrappers may lead to different behavior when one component is changed for another, which might lead to failures in the field. This work reports on a simple, yet effective approach to detect these differences. The approach is based on tool-supported reviews leveraging lightweight static analysis and machine learning. The approach is evaluated in a case study that analyzes NASA's Operating System Abstraction Layer (OS AL), which is used in various space missions. We detected 84 corner-case issues of which 57 turned out to be bugs that could have resulted in runtime failures

Real-time Outlier Detection using Unbounded Data Streaming and Machine Learning

Accelerated advancements in technology, the Internet of Things, and cloud computing have spurred an emergence of unstructured data that is contributing to rapid growth in data volumes. No human can manage to keep up with monitoring and analyzing these unbounded data streams and thus predictive and analytic tools are needed. By leveraging machine learning this data can be converted into insights which are enabling datadriven decisions that can drastically accelerate innovation, improve user experience, and drive operational efficiency. The purpose of this thesis is to design and implement a system for real-time outlier detection using unbounded data streams and machine learning. Traditionally, this is accomplished by using alarm-thresholds on important system metrics. Yet, a static threshold cannot account for changes in trends and seasonality, changes in the system, or an increased system load. Thus, the intention is to leverage machine learning to instead look for deviations in the behavior of the data not caused by natural changes but by malfunctions. The use-case driving the thesis forward is real-time outlier detection in a Content Delivery Network (CDN). The input data includes Http-error messages received by clients, and contextual information like region, cache domains, and error codes, to provide tailormade predictions accounting for the trends in the data. The outlier detection system consists of a data collection pipeline leveraging the technique of stream processing, a MiniBatchKMeans clustering model that provides online clustering of incoming data according to their similar characteristics, and an LSTM AutoEncoder that accounts for temporal nature of the data and detects outlier data points in the clusters. An important finding is that an outlier is defined as an abnormal amount of outlier data points all originating from the same cluster, not a single outlier data point. Thus, the alerting system will be implementing an outlier percentage threshold. The experimental results show that an outlier is detected within one minute from a cache break-down. This triggers an alert to the system owners, containing graphs of the clustered data to narrow down the search area of the cause to enable preventive action towards the prominent incident. Further results show that within 2 minutes from fixing the cause the system will provide feedback that the actions taken were successful. Considering the real-time requirements of the CDN environment, it is concluded that the short delay for detection is indeed real-time. Proving that machine learning is indeed able to detect outliers in unbounded data streams in a real-time manner. Further analysis shows that the system is more accurate during peakhours when more data is in circulation than during none peak-hours, despite the temporal LSTM layers. Presumably, an effect from the model needing to train on more data to better account for seasonality and trends. Future work necessary to put the outlier detection system in production thus includes more training to improve accuracy and correctness. Furthermore, one could consider implementing necessary functionality for a production environment and possibly adding enhancing features that can automatically avert incidents detected and handle the causes of them

Ambiguity in Requirements Engineering: Towards a Unifying Framework

A long stream of research in RE has been devoted to analyzing the occurrences and consequences of ambiguity in requirements documents. Ambiguity often occurs in documents, most often in natural language (NL) ones, but occasionally also in formal specifications, be it because of abstraction, or of imprecise designation of which real-world entities are denotated by certain expressions. In many of those studies, ambiguity has been considered a defect to be avoided. In this paper, we investigate the nature of ambiguity, and advocate that the simplistic view of ambiguity as merely a defect in the document does not do justice to the complexity of this phenomenon. We offer a more extensive analysis, based on the multiple linguistic sources of ambiguity, and present a list of real-world cases, both in written matter and in oral interviews, that we analyze based on our framework. We hope that a better understanding of the phenomenon can help in the analysis of practical experiences and in the design of more effective methods to detect, mark and handle ambiguity