A Human-Centric Approach to Program Understanding

Software development is a large global industry, but software products continue to ship with known and unknown defects [60]. In the US, such defects cost firms many billions of dollars annually by compromising security, privacy, and functionality [73]. To mitigate this expense, recent research has focused on finding specific errors in code (e.g., [13, 25, 29, 34, 35, 47, 48, 61, 66, 86]). These important analyses hold out the possibility of identifying many types of implementation issues, but they fail to address a problem underlying all of them: software is difficult to understand. Professional software developers spend over 75% of their time trying to understand code [45, 76]. Reading code is the most time consuming part [31, 39, 78, 85] of the most expensive activity [77, 87] in the software development process. Yet, software comprehension as an activity is poorly understood by both researchers and practitioners [74, 106]. Our research seeks to develop a general and practical approach for analyzing program understandability from the perspective of real humans. In addition, we propose to develop tools for mechanically generating documentation in order to make programs easier to understand. We will focus on three key dimensions of program understandability: readability, a local judgment of how easy code is to understand; runtime behavior, a characterization of what a program was designed to do; and documentation, non-code text that aids in program understanding. Our key technical insight lies in combining multiple surface features (e.g., identifier length or number of assignment statements) to characterize aspects of programs that lack precise semantics. The use of lightweight features permits our techniques to scale to large programs and generalize across multiple application domains. Additionally, we will continue to pioneer techniques [19] for generating output that is directly comparable to real-world human-created documentation. This is useful for evaluation, but also suggests that our proposed tools could be readily integrated into current software engineering practice. Software understandability becomes increasingly important as the number and size of software projects grow: as complexity increases, it becomes paramount to comprehend software and use it correctly. Fred Brooks once noted that “the most radical possible solution for constructing software is not to construct it at all” [16], and instead assemble already-constructed pieces. Code reuse and composition are becoming increasingly important: a recent study found that a set of programs was comprised of 32% re-used code (not including libraries) [88], whereas a similar 1987 study estimated the figure at only 5% [38]. In 2005, a NASA survey found that the most significant barrier to reuse is that software is too difficult to understand or is poorly documented [42] — above even requirements or compatibility. In a future where software engineering focus shifts from implementation to design and composition concerns, program understandability will become even more important.