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Jentzsch, Sophie F., Hochgeschwender, Nico.  2019.  Don't Forget Your Roots! Using Provenance Data for Transparent and Explainable Development of Machine Learning Models. 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW). :37–40.
Explaining reasoning and behaviour of artificial intelligent systems to human users becomes increasingly urgent, especially in the field of machine learning. Many recent contributions approach this issue with post-hoc methods, meaning they consider the final system and its outcomes, while the roots of included artefacts are widely neglected. However, we argue in this position paper that there needs to be a stronger focus on the development process. Without insights into specific design decisions and meta information that accrue during the development an accurate explanation of the resulting model is hardly possible. To remedy this situation we propose to increase process transparency by applying provenance methods, which serves also as a basis for increased explainability.
Nomura, Komei, Rikitake, Kenji, Matsumoto, Ryosuke.  2019.  Automatic Whitelist Generation for SQL Queries Using Web Application Tests. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:465–470.

Stealing confidential information from a database has become a severe vulnerability issue for web applications. The attacks can be prevented by defining a whitelist of SQL queries issued by web applications and detecting queries not in list. For large-scale web applications, automated generation of the whitelist is conducted because manually defining numerous query patterns is impractical for developers. Conventional methods for automated generation are unable to detect attacks immediately because of the long time required for collecting legitimate queries. Moreover, they require application-specific implementations that reduce the versatility of the methods. As described herein, we propose a method to generate a whitelist automatically using queries issued during web application tests. Our proposed method uses the queries generated during application tests. It is independent of specific applications, which yields improved timeliness against attacks and versatility for multiple applications.

Obster, M., Kowalewski, S..  2017.  A live static code analysis architecture for PLC software. 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). :1–4.

Static code analysis is a convenient technique to support the development of software. Without prior test setup, information about a later runtime behavior can be inferred and errors in the code can be found before using a regular compiler. Solutions to apply static code analysis to PLC software following the IEC 61131-3 already exist, but using these separate tools usually creates a gap in the development process. In this paper we introduce an architecture to use static analysis directly in a development environment and give instant feedback to the developer while he is still editing the PLC software.

Honig, William L., Noda, Natsuko, Takada, Shingo.  2016.  Lack of Attention to Singular (or Atomic) Requirements Despite Benefits for Quality, Metrics and Management. SIGSOFT Softw. Eng. Notes. 41:1–5.

There are seemingly many advantages to being able to identify, document, test, and trace single or "atomic" requirements. Why then has there been little attention to the topic and no widely used definition or process on how to define atomic requirements? Definitions of requirements and standards focus on user needs, system capabilities or functions; some definitions include making individual requirements singular or without the use of conjunctions. In a few cases there has been a description of atomic system events or requirements. This work is surveyed here although there is no well accepted and used best practice for generating atomic requirements. Due to their importance in software engineering, quality and metrics for requirements have received considerable attention. In the seminal paper on software requirements quality, Davis et al. proposed specific metrics including the "unambiguous quality factor" and the "verifiable quality factor"; these and other metrics work best with a clearly enumerable list of single requirements. Atomic requirements are defined here as a natural language statement that completely describes a single system function, feature, need, or capability, including all information, details, limits, and characteristics. A typical user login screen is used as an example of an atomic requirement which can include both functional and nonfunctional requirements. Individual atomic requirements are supported by a system glossary, references to applicable industry standards, mock ups of the user interface, etc. One way to identify such atomic requirements is from use case or system event analysis. This definition of atomic requirements is still a work in progress and offered to prompt discussion. Atomic requirements allow clear naming or numbering of requirements for traceability, change management, and importance ranking. Further, atomic requirements defined in this manner are suitable for rapid implementation approaches (implementing one requirement at a time), enable good test planning (testing can clearly indicate pass or fail of the whole requirement), and offer other management advantages in project control.