Search

open access

Editorial

Automated testing tools typically create test cases that are different from what human testers create. This often makes the tools less effective, the created tests harder to understand, and thus results in tools providing less support to human testers. Here, we propose a framework based on cognitive science and, in particular, an analysis of approaches to problem-solving, for identifying cognitive processes of testers. The framework helps map test design steps and criteria used in human test activities and thus to better understand how effective human testers perform their tasks. Ultimately, our goal is to be able to mimic how humans create test cases and thus to design more human-like automated test generation systems. We posit that such systems can better augment and support testers in a way that is meaningful to them., Software Center: Aspects of Automated Testing, XIVT - eXcellence in Variant Testing, Software Center, VeriDevOps - Automated Protection and Prevention to Meet Security Requirements in DevOps Environments

Testing applications with graphical user Interfaces (GUI) is an important but also a time-consuming task in practice. Tools and frameworks for GUI test automation can make the test execution more efficient and lower the manual labor required for regression testing. However, the test scripts used for automated GUI-based testing still require a substantial development effort and are often reported as sensitive to change, leading to frequent and costly maintenance. The efficiency of development, maintenance, and evolution of such tests are thereby dependent on the readability of scripts and the ease-of-use of test tools/frameworks in which the test scripts are defined. To address these shortcomings in existing state-of-practice techniques, a novel technique referred to as Augmented Testing (AT) has been proposed. AT is defined as testing the System Under Test (SUT) through an Augmented GUI that superimposes information on top of the SUT GUI. The Augmented GUI can provide the user with hints, test data, or other support while also observing and recording the tester's interactions. For this study, a prototype tool, called Scout, has been used that adheres to the AT concept that is evaluated in an industrial empirical study. In the evaluation, quasi-experiments and questionnaire surveys are performed in two workshops, with 12 practitioners from two Swedish companies (Ericsson and Inceptive). Results show that Scout can be used to create equivalent test cases faster, with statistical significance, than creating automated scripts in two popular state-of-practice tools. The study concludes that AT has cost-value benefits, applies to industrial-grade software, and overcomes several deficiencies of state-of-practice GUI testing technologies in terms of ease-of-use. © 2020 IEEE.

Software testing is a complex, intellectual activity based (at least) on analysis, reasoning, decision making, abstraction and collaboration performed in a highly demanding environment. Naturally, it uses and allocates multiple cognitive resources in software testers. However, while a cognitive psychology perspective is increasingly used in the general software engineering literature, it has yet to find its place in software testing. To the best of our knowledge, no theory of software testers’ cognitive processes exists. Here, we take a first step towards such a theory by presenting a cognitive model of software testing based on how problem solving is conceptualized in cognitive psychology. Our approach is to instantiate a general problem solving process for the specific problem of creating test cases. We then propose a controlled experiment for testing our cognitive test design model. The experiment makes use of verbal protocol analysis to understand the mechanisms by which human testers choose, design, implement and evaluate test cases and test design strategies. An initial evaluation was then performed with five students as subjects. The results support a problem solving-based model of test design for capturing testers’ cognitive processes., Software Center: Aspects of Automated Testing, XIVT - eXcellence in Variant Testing

QC 20190903

Deep Learning (DL) systems are rapidly being adopted in safety and security critical domains, urgently calling for ways to test their correctness and robustness. Testing of DL systems has traditionally relied on manual collection and labelling of data. Recently, a number of coverage criteria based on neuron activation values have been proposed. These criteria essentially count the number of neurons whose activation during the execution of a DL system satisfied certain properties, such as being above predefined thresholds. However, existing coverage criteria are not sufficiently fine grained to capture subtle behaviours exhibited by DL systems. Moreover, evaluations have focused on showing correlation between adversarial examples and proposed criteria rather than evaluating and guiding their use for actual testing of DL systems. We propose a novel test adequacy criterion for testing of DL systems, called Surprise Adequacy for Deep Learning Systems (SADL), which is based on the behaviour of DL systems with respect to their training data. We measure the surprise of an input as the difference in DL system's behaviour between the input and the training data (i.e., what was learnt during training), and subsequently develop this as an adequacy criterion: a good test input should be sufficiently but not overtly surprising compared to training data. Empirical evaluation using a range of DL systems from simple image classifiers to autonomous driving car platforms shows that systematic sampling of inputs based on their surprise can improve classification accuracy of DL systems against adversarial examples by up to 77.5% via retraining.

Attaining effective communication within and across organizational units is among the most critical challenges for success in software development organizations. This paper presents a novel model, supporting analysis of problems in inter-departmental communication events. The model was developed and designed based on industrial needs emphasizing flexibility, applicability and scalability. The model covers central communication aspects in order to provide a useful approximation of communication problems rather than in-depth modeling on message-by message basis. Other event-specific information, such as costs, can then be attached to enrich analysis and understanding. To exemplify and evaluate the model and collect feedback from industry, it was applied to 16 events at a Swedish automotive manufacturer where communication between two departments had broken down during development of software-intensive systems. The evaluation showed that the model helped structure and conduct systematic data collection and analysis of dysfunctional communication patterns. We found that insufficient understanding of the matters being communicated was prevalent, but also more specifically, requirements were insufficiently balanced, detailed and specified over the full system development cycle. Besides, the long-term cost for the company was analyzed in depth for each event, yielding a total estimated cost for the analyzed communication events of 11.2MUS$. © 2019 World Scientific Publishing Company.

There are studies showing what happens if agile teams are introduced into a non-agile organization, e.g. higher overhead costs and the necessity of an understanding of agile methods even outside the teams. This case study shows an example of work motivational aspects that might surface when an agile team exists in the middle of a more traditional structure. This case study was conducted at a car manufacturer in Sweden, consisting of an unstructured interview with the Scrum Master and a semi-structured focus group. The results show that the teams felt that the feedback from the surrounding organization was unsynchronized resulting in them not feeling appreciated when delivering their work. Moreover, they felt frustrated when working on non-agile teams after have been working on agile ones. This study concludes that there were work motivational affects of fitting an agile team into a non-agile surrounding organization, and therefore this might also be true for other organizations.

The focus on psychology has increased within software engineering due to the project management innovation "agile development processes". The agile methods do not explicitly consider group development aspects; they simply assume what is described in group psychology as mature groups. This study was conducted with 45 employees and their twelve managers (N=57) from two SAP customers in the US that were working with agile methods, and the data were collected via an online survey. The selected Agility measurement was correlated to a Group Development measurement and showed significant convergent validity, i.e., a more mature team is also a more agile team. This means that the agile methods probably would benefit from taking group development into account when its practices are being introduced.

Agile development has now become a well-known approach to collaboration in professional work life. Both researchers and practitioners want validated tools to measure agility. This study sets out to validate an agile maturity measurement model with statistical tests and empirical data. First, a pretest was conducted as a case study including a survey and focus group. Second, the main study was conducted with 45 employees from two SAP customers in the US. We used internal consistency (by a Cronbach's alpha) as the main measure for reliability and analyzed construct validity by exploratory principal factor analysis (PFA). The results suggest a new categorization of a subset of items existing in the tool and provides empirical support for these new groups of factors. However, we argue that more work is needed to reach the point where a maturity models with quantitative data can be said to validly measure agility, and even then, such a measurement still needs to include some deeper analysis with cultural and contextual items.